In [1]:
import os
from tqdm import tqdm
In [2]:
datapath='C:/Users/Ravi/Desktop/Cadla.ai assignment/find_phone'
In [3]:
from PIL import Image
import matplotlib.pyplot as plt
%matplotlib inline
photo_loc_mapping={}
input_data=[]
labels=[]
def preprocess_photo(photopath,name):
img=Image.open(photopath+'/'+name)
input_data.append(img)
labels.append(photo_loc_mapping[name])
#plt.imshow(img)
#plt.show()
#img.show()
return
In [4]:
def get_data(datapath):
# First load txt file
for f in os.listdir(datapath):
if f.endswith('.txt'):
with open(datapath+'/'+f) as txt_file:
lines=txt_file.readlines()
#print(lines)
for line in lines:
line_data=line.split()
photo_loc_mapping[line_data[0]]=[float(line_data[1]),float(line_data[2])]
for f in os.listdir(datapath):
if f.endswith('.jpg'):
preprocess_photo(datapath,f)
return
get_data(datapath)
In [5]:
# visualize input_data
from PIL import ImageDraw
def visualize(img_arr,loc):
width, height = img_arr.size
#print(loc, img_arr.size)
#print("Line 1: ",(0,int(float(loc[1])*height), width,int(float(loc[1])*height))
#print("Line 2: ",(int(float(loc[0])*width),0,int(float(loc[0])*width),height))
draw = ImageDraw.Draw(img_arr)
#print(0,int(float(loc[0])*width), height,int(float(loc[0])*width))
#print(0,int(float(loc[1])*height), width,int(float(loc[1])*height))
#print(int(float(loc[0])*width),0,int(float(loc[0])*width),height)
x=int(float(loc[1])*height)
y=int(float(loc[0])*width)
r=int(loc[0]*0.05)+30
#draw.line((0,x, width,x), fill=124,width=1)
#draw.line((y,0,y,height), fill=124,width=1)
print(x-r, y-r, x+r, y+r)
draw.ellipse((y-r, x-r, y+r, x+r),outline ='blue')
plt.imshow(img_arr)
plt.show()
for photo,label in zip(input_data,labels):
visualize(photo,label)
14 376 74 436
112 210 172 270
248 371 308 431
181 68 241 128
174 192 234 252
220 92 280 152
52 332 112 392
245 253 305 313
94 298 154 358
124 84 184 144
131 207 191 267
54 369 114 429
196 369 256 429
158 244 218 304
205 193 265 253
244 182 304 242
133 222 193 282
190 362 250 422
76 347 136 407
227 190 287 250
42 288 102 348
128 229 188 289
116 207 176 267
250 60 310 120
202 379 262 439
254 82 314 142
233 77 293 137
143 200 203 260
24 344 84 404
242 405 302 465
44 190 104 250
263 80 323 140
66 310 126 370
57 63 117 123
60 156 120 216
180 43 240 103
181 354 241 414
115 111 175 171
35 85 95 145
124 276 184 336
88 75 148 135
261 114 321 174
76 254 136 314
134 215 194 275
82 296 142 356
224 139 284 199
156 68 216 128
171 173 231 233
69 371 129 431
52 247 112 307
211 94 271 154
69 276 129 336
88 73 148 133
196 305 256 365
91 43 151 103
76 285 136 345
11 283 71 343
82 24 142 84
73 104 133 164
175 180 235 240
214 381 274 441
186 386 246 446
232 97 292 157
78 268 138 328
168 295 228 355
177 307 237 367
207 60 267 120
235 342 295 402
95 296 155 356
146 28 206 88
113 403 173 463
119 225 179 285
5 62 65 122
7 264 67 324
180 393 240 453
39 384 99 444
223 170 283 230
165 87 225 147
61 236 121 296
-5 134 55 194
214 305 274 365
122 129 182 189
173 403 233 463
20 384 80 444
202 136 262 196
223 143 283 203
87 43 147 103
82 134 142 194
50 45 110 105
242 117 302 177
242 102 302 162
146 369 206 429
241 -8 301 52
14 16 74 76
193 89 253 149
26 28 86 88
50 176 110 236
208 386 268 446
91 305 151 365
144 202 204 262
39 94 99 154
213 352 273 412
201 369 261 429
134 185 194 245
128 339 188 399
116 320 176 380
82 290 142 350
60 345 120 405
253 214 313 274
11 263 71 323
39 256 99 316
202 318 262 378
52 60 112 120
42 85 102 145
63 112 123 172
76 126 136 186
88 102 148 162
63 149 123 209
137 100 197 160
42 217 102 277
199 283 259 343
57 364 117 424
115 136 175 196
72 94 132 154
245 200 305 260
109 340 169 400
232 237 292 297
143 151 203 211
In [6]:
from keras.activations import relu
from keras.callbacks import EarlyStopping,TensorBoard, ModelCheckpoint
from keras.layers import *
from keras.optimizers import adam
from keras.losses import mse
from keras.models import Sequential,Model
from keras.utils import to_categorical
import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
%matplotlib inline
from sklearn.utils import shuffle
import numpy as np
Using TensorFlow backend.
In [7]:
#np.array(input_data[0])
In [8]:
from keras.models import Model
from keras.applications import mobilenetv2
from keras.applications.mobilenetv2 import preprocess_input
model_mobile=mobilenetv2.MobileNetV2(input_shape=(224,224,3), include_top=True, weights='imagenet',classes=1000)
WARNING:tensorflow:From C:\Users\Ravi\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer.
In [9]:
model2=Model(input=model_mobile.input,output=model_mobile.layers[-2].output)
print(model2.summary())
print(model2.output_shape)
C:\Users\Ravi\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor("in..., outputs=Tensor("gl...)`
"""Entry point for launching an IPython kernel.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
__________________________________________________________________________________________________
Conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
Conv1 (Conv2D) (None, 112, 112, 32) 864 Conv1_pad[0][0]
__________________________________________________________________________________________________
bn_Conv1 (BatchNormalization) (None, 112, 112, 32) 128 Conv1[0][0]
__________________________________________________________________________________________________
Conv1_relu (ReLU) (None, 112, 112, 32) 0 bn_Conv1[0][0]
__________________________________________________________________________________________________
expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288 Conv1_relu[0][0]
__________________________________________________________________________________________________
expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128 expanded_conv_depthwise[0][0]
__________________________________________________________________________________________________
expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0 expanded_conv_depthwise_BN[0][0]
__________________________________________________________________________________________________
expanded_conv_project (Conv2D) (None, 112, 112, 16) 512 expanded_conv_depthwise_relu[0][0
__________________________________________________________________________________________________
expanded_conv_project_BN (Batch (None, 112, 112, 16) 64 expanded_conv_project[0][0]
__________________________________________________________________________________________________
block_1_expand (Conv2D) (None, 112, 112, 96) 1536 expanded_conv_project_BN[0][0]
__________________________________________________________________________________________________
block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384 block_1_expand[0][0]
__________________________________________________________________________________________________
block_1_expand_relu (ReLU) (None, 112, 112, 96) 0 block_1_expand_BN[0][0]
__________________________________________________________________________________________________
block_1_pad (ZeroPadding2D) (None, 113, 113, 96) 0 block_1_expand_relu[0][0]
__________________________________________________________________________________________________
block_1_depthwise (DepthwiseCon (None, 56, 56, 96) 864 block_1_pad[0][0]
__________________________________________________________________________________________________
block_1_depthwise_BN (BatchNorm (None, 56, 56, 96) 384 block_1_depthwise[0][0]
__________________________________________________________________________________________________
block_1_depthwise_relu (ReLU) (None, 56, 56, 96) 0 block_1_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_1_project (Conv2D) (None, 56, 56, 24) 2304 block_1_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_1_project_BN (BatchNormal (None, 56, 56, 24) 96 block_1_project[0][0]
__________________________________________________________________________________________________
block_2_expand (Conv2D) (None, 56, 56, 144) 3456 block_1_project_BN[0][0]
__________________________________________________________________________________________________
block_2_expand_BN (BatchNormali (None, 56, 56, 144) 576 block_2_expand[0][0]
__________________________________________________________________________________________________
block_2_expand_relu (ReLU) (None, 56, 56, 144) 0 block_2_expand_BN[0][0]
__________________________________________________________________________________________________
block_2_depthwise (DepthwiseCon (None, 56, 56, 144) 1296 block_2_expand_relu[0][0]
__________________________________________________________________________________________________
block_2_depthwise_BN (BatchNorm (None, 56, 56, 144) 576 block_2_depthwise[0][0]
__________________________________________________________________________________________________
block_2_depthwise_relu (ReLU) (None, 56, 56, 144) 0 block_2_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_2_project (Conv2D) (None, 56, 56, 24) 3456 block_2_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_2_project_BN (BatchNormal (None, 56, 56, 24) 96 block_2_project[0][0]
__________________________________________________________________________________________________
block_2_add (Add) (None, 56, 56, 24) 0 block_1_project_BN[0][0]
block_2_project_BN[0][0]
__________________________________________________________________________________________________
block_3_expand (Conv2D) (None, 56, 56, 144) 3456 block_2_add[0][0]
__________________________________________________________________________________________________
block_3_expand_BN (BatchNormali (None, 56, 56, 144) 576 block_3_expand[0][0]
__________________________________________________________________________________________________
block_3_expand_relu (ReLU) (None, 56, 56, 144) 0 block_3_expand_BN[0][0]
__________________________________________________________________________________________________
block_3_pad (ZeroPadding2D) (None, 57, 57, 144) 0 block_3_expand_relu[0][0]
__________________________________________________________________________________________________
block_3_depthwise (DepthwiseCon (None, 28, 28, 144) 1296 block_3_pad[0][0]
__________________________________________________________________________________________________
block_3_depthwise_BN (BatchNorm (None, 28, 28, 144) 576 block_3_depthwise[0][0]
__________________________________________________________________________________________________
block_3_depthwise_relu (ReLU) (None, 28, 28, 144) 0 block_3_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_3_project (Conv2D) (None, 28, 28, 32) 4608 block_3_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_3_project_BN (BatchNormal (None, 28, 28, 32) 128 block_3_project[0][0]
__________________________________________________________________________________________________
block_4_expand (Conv2D) (None, 28, 28, 192) 6144 block_3_project_BN[0][0]
__________________________________________________________________________________________________
block_4_expand_BN (BatchNormali (None, 28, 28, 192) 768 block_4_expand[0][0]
__________________________________________________________________________________________________
block_4_expand_relu (ReLU) (None, 28, 28, 192) 0 block_4_expand_BN[0][0]
__________________________________________________________________________________________________
block_4_depthwise (DepthwiseCon (None, 28, 28, 192) 1728 block_4_expand_relu[0][0]
__________________________________________________________________________________________________
block_4_depthwise_BN (BatchNorm (None, 28, 28, 192) 768 block_4_depthwise[0][0]
__________________________________________________________________________________________________
block_4_depthwise_relu (ReLU) (None, 28, 28, 192) 0 block_4_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_4_project (Conv2D) (None, 28, 28, 32) 6144 block_4_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_4_project_BN (BatchNormal (None, 28, 28, 32) 128 block_4_project[0][0]
__________________________________________________________________________________________________
block_4_add (Add) (None, 28, 28, 32) 0 block_3_project_BN[0][0]
block_4_project_BN[0][0]
__________________________________________________________________________________________________
block_5_expand (Conv2D) (None, 28, 28, 192) 6144 block_4_add[0][0]
__________________________________________________________________________________________________
block_5_expand_BN (BatchNormali (None, 28, 28, 192) 768 block_5_expand[0][0]
__________________________________________________________________________________________________
block_5_expand_relu (ReLU) (None, 28, 28, 192) 0 block_5_expand_BN[0][0]
__________________________________________________________________________________________________
block_5_depthwise (DepthwiseCon (None, 28, 28, 192) 1728 block_5_expand_relu[0][0]
__________________________________________________________________________________________________
block_5_depthwise_BN (BatchNorm (None, 28, 28, 192) 768 block_5_depthwise[0][0]
__________________________________________________________________________________________________
block_5_depthwise_relu (ReLU) (None, 28, 28, 192) 0 block_5_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_5_project (Conv2D) (None, 28, 28, 32) 6144 block_5_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_5_project_BN (BatchNormal (None, 28, 28, 32) 128 block_5_project[0][0]
__________________________________________________________________________________________________
block_5_add (Add) (None, 28, 28, 32) 0 block_4_add[0][0]
block_5_project_BN[0][0]
__________________________________________________________________________________________________
block_6_expand (Conv2D) (None, 28, 28, 192) 6144 block_5_add[0][0]
__________________________________________________________________________________________________
block_6_expand_BN (BatchNormali (None, 28, 28, 192) 768 block_6_expand[0][0]
__________________________________________________________________________________________________
block_6_expand_relu (ReLU) (None, 28, 28, 192) 0 block_6_expand_BN[0][0]
__________________________________________________________________________________________________
block_6_pad (ZeroPadding2D) (None, 29, 29, 192) 0 block_6_expand_relu[0][0]
__________________________________________________________________________________________________
block_6_depthwise (DepthwiseCon (None, 14, 14, 192) 1728 block_6_pad[0][0]
__________________________________________________________________________________________________
block_6_depthwise_BN (BatchNorm (None, 14, 14, 192) 768 block_6_depthwise[0][0]
__________________________________________________________________________________________________
block_6_depthwise_relu (ReLU) (None, 14, 14, 192) 0 block_6_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_6_project (Conv2D) (None, 14, 14, 64) 12288 block_6_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_6_project_BN (BatchNormal (None, 14, 14, 64) 256 block_6_project[0][0]
__________________________________________________________________________________________________
block_7_expand (Conv2D) (None, 14, 14, 384) 24576 block_6_project_BN[0][0]
__________________________________________________________________________________________________
block_7_expand_BN (BatchNormali (None, 14, 14, 384) 1536 block_7_expand[0][0]
__________________________________________________________________________________________________
block_7_expand_relu (ReLU) (None, 14, 14, 384) 0 block_7_expand_BN[0][0]
__________________________________________________________________________________________________
block_7_depthwise (DepthwiseCon (None, 14, 14, 384) 3456 block_7_expand_relu[0][0]
__________________________________________________________________________________________________
block_7_depthwise_BN (BatchNorm (None, 14, 14, 384) 1536 block_7_depthwise[0][0]
__________________________________________________________________________________________________
block_7_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_7_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_7_project (Conv2D) (None, 14, 14, 64) 24576 block_7_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_7_project_BN (BatchNormal (None, 14, 14, 64) 256 block_7_project[0][0]
__________________________________________________________________________________________________
block_7_add (Add) (None, 14, 14, 64) 0 block_6_project_BN[0][0]
block_7_project_BN[0][0]
__________________________________________________________________________________________________
block_8_expand (Conv2D) (None, 14, 14, 384) 24576 block_7_add[0][0]
__________________________________________________________________________________________________
block_8_expand_BN (BatchNormali (None, 14, 14, 384) 1536 block_8_expand[0][0]
__________________________________________________________________________________________________
block_8_expand_relu (ReLU) (None, 14, 14, 384) 0 block_8_expand_BN[0][0]
__________________________________________________________________________________________________
block_8_depthwise (DepthwiseCon (None, 14, 14, 384) 3456 block_8_expand_relu[0][0]
__________________________________________________________________________________________________
block_8_depthwise_BN (BatchNorm (None, 14, 14, 384) 1536 block_8_depthwise[0][0]
__________________________________________________________________________________________________
block_8_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_8_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_8_project (Conv2D) (None, 14, 14, 64) 24576 block_8_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_8_project_BN (BatchNormal (None, 14, 14, 64) 256 block_8_project[0][0]
__________________________________________________________________________________________________
block_8_add (Add) (None, 14, 14, 64) 0 block_7_add[0][0]
block_8_project_BN[0][0]
__________________________________________________________________________________________________
block_9_expand (Conv2D) (None, 14, 14, 384) 24576 block_8_add[0][0]
__________________________________________________________________________________________________
block_9_expand_BN (BatchNormali (None, 14, 14, 384) 1536 block_9_expand[0][0]
__________________________________________________________________________________________________
block_9_expand_relu (ReLU) (None, 14, 14, 384) 0 block_9_expand_BN[0][0]
__________________________________________________________________________________________________
block_9_depthwise (DepthwiseCon (None, 14, 14, 384) 3456 block_9_expand_relu[0][0]
__________________________________________________________________________________________________
block_9_depthwise_BN (BatchNorm (None, 14, 14, 384) 1536 block_9_depthwise[0][0]
__________________________________________________________________________________________________
block_9_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_9_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_9_project (Conv2D) (None, 14, 14, 64) 24576 block_9_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_9_project_BN (BatchNormal (None, 14, 14, 64) 256 block_9_project[0][0]
__________________________________________________________________________________________________
block_9_add (Add) (None, 14, 14, 64) 0 block_8_add[0][0]
block_9_project_BN[0][0]
__________________________________________________________________________________________________
block_10_expand (Conv2D) (None, 14, 14, 384) 24576 block_9_add[0][0]
__________________________________________________________________________________________________
block_10_expand_BN (BatchNormal (None, 14, 14, 384) 1536 block_10_expand[0][0]
__________________________________________________________________________________________________
block_10_expand_relu (ReLU) (None, 14, 14, 384) 0 block_10_expand_BN[0][0]
__________________________________________________________________________________________________
block_10_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 block_10_expand_relu[0][0]
__________________________________________________________________________________________________
block_10_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 block_10_depthwise[0][0]
__________________________________________________________________________________________________
block_10_depthwise_relu (ReLU) (None, 14, 14, 384) 0 block_10_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_10_project (Conv2D) (None, 14, 14, 96) 36864 block_10_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_10_project_BN (BatchNorma (None, 14, 14, 96) 384 block_10_project[0][0]
__________________________________________________________________________________________________
block_11_expand (Conv2D) (None, 14, 14, 576) 55296 block_10_project_BN[0][0]
__________________________________________________________________________________________________
block_11_expand_BN (BatchNormal (None, 14, 14, 576) 2304 block_11_expand[0][0]
__________________________________________________________________________________________________
block_11_expand_relu (ReLU) (None, 14, 14, 576) 0 block_11_expand_BN[0][0]
__________________________________________________________________________________________________
block_11_depthwise (DepthwiseCo (None, 14, 14, 576) 5184 block_11_expand_relu[0][0]
__________________________________________________________________________________________________
block_11_depthwise_BN (BatchNor (None, 14, 14, 576) 2304 block_11_depthwise[0][0]
__________________________________________________________________________________________________
block_11_depthwise_relu (ReLU) (None, 14, 14, 576) 0 block_11_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_11_project (Conv2D) (None, 14, 14, 96) 55296 block_11_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_11_project_BN (BatchNorma (None, 14, 14, 96) 384 block_11_project[0][0]
__________________________________________________________________________________________________
block_11_add (Add) (None, 14, 14, 96) 0 block_10_project_BN[0][0]
block_11_project_BN[0][0]
__________________________________________________________________________________________________
block_12_expand (Conv2D) (None, 14, 14, 576) 55296 block_11_add[0][0]
__________________________________________________________________________________________________
block_12_expand_BN (BatchNormal (None, 14, 14, 576) 2304 block_12_expand[0][0]
__________________________________________________________________________________________________
block_12_expand_relu (ReLU) (None, 14, 14, 576) 0 block_12_expand_BN[0][0]
__________________________________________________________________________________________________
block_12_depthwise (DepthwiseCo (None, 14, 14, 576) 5184 block_12_expand_relu[0][0]
__________________________________________________________________________________________________
block_12_depthwise_BN (BatchNor (None, 14, 14, 576) 2304 block_12_depthwise[0][0]
__________________________________________________________________________________________________
block_12_depthwise_relu (ReLU) (None, 14, 14, 576) 0 block_12_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_12_project (Conv2D) (None, 14, 14, 96) 55296 block_12_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_12_project_BN (BatchNorma (None, 14, 14, 96) 384 block_12_project[0][0]
__________________________________________________________________________________________________
block_12_add (Add) (None, 14, 14, 96) 0 block_11_add[0][0]
block_12_project_BN[0][0]
__________________________________________________________________________________________________
block_13_expand (Conv2D) (None, 14, 14, 576) 55296 block_12_add[0][0]
__________________________________________________________________________________________________
block_13_expand_BN (BatchNormal (None, 14, 14, 576) 2304 block_13_expand[0][0]
__________________________________________________________________________________________________
block_13_expand_relu (ReLU) (None, 14, 14, 576) 0 block_13_expand_BN[0][0]
__________________________________________________________________________________________________
block_13_pad (ZeroPadding2D) (None, 15, 15, 576) 0 block_13_expand_relu[0][0]
__________________________________________________________________________________________________
block_13_depthwise (DepthwiseCo (None, 7, 7, 576) 5184 block_13_pad[0][0]
__________________________________________________________________________________________________
block_13_depthwise_BN (BatchNor (None, 7, 7, 576) 2304 block_13_depthwise[0][0]
__________________________________________________________________________________________________
block_13_depthwise_relu (ReLU) (None, 7, 7, 576) 0 block_13_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_13_project (Conv2D) (None, 7, 7, 160) 92160 block_13_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_13_project_BN (BatchNorma (None, 7, 7, 160) 640 block_13_project[0][0]
__________________________________________________________________________________________________
block_14_expand (Conv2D) (None, 7, 7, 960) 153600 block_13_project_BN[0][0]
__________________________________________________________________________________________________
block_14_expand_BN (BatchNormal (None, 7, 7, 960) 3840 block_14_expand[0][0]
__________________________________________________________________________________________________
block_14_expand_relu (ReLU) (None, 7, 7, 960) 0 block_14_expand_BN[0][0]
__________________________________________________________________________________________________
block_14_depthwise (DepthwiseCo (None, 7, 7, 960) 8640 block_14_expand_relu[0][0]
__________________________________________________________________________________________________
block_14_depthwise_BN (BatchNor (None, 7, 7, 960) 3840 block_14_depthwise[0][0]
__________________________________________________________________________________________________
block_14_depthwise_relu (ReLU) (None, 7, 7, 960) 0 block_14_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_14_project (Conv2D) (None, 7, 7, 160) 153600 block_14_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_14_project_BN (BatchNorma (None, 7, 7, 160) 640 block_14_project[0][0]
__________________________________________________________________________________________________
block_14_add (Add) (None, 7, 7, 160) 0 block_13_project_BN[0][0]
block_14_project_BN[0][0]
__________________________________________________________________________________________________
block_15_expand (Conv2D) (None, 7, 7, 960) 153600 block_14_add[0][0]
__________________________________________________________________________________________________
block_15_expand_BN (BatchNormal (None, 7, 7, 960) 3840 block_15_expand[0][0]
__________________________________________________________________________________________________
block_15_expand_relu (ReLU) (None, 7, 7, 960) 0 block_15_expand_BN[0][0]
__________________________________________________________________________________________________
block_15_depthwise (DepthwiseCo (None, 7, 7, 960) 8640 block_15_expand_relu[0][0]
__________________________________________________________________________________________________
block_15_depthwise_BN (BatchNor (None, 7, 7, 960) 3840 block_15_depthwise[0][0]
__________________________________________________________________________________________________
block_15_depthwise_relu (ReLU) (None, 7, 7, 960) 0 block_15_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_15_project (Conv2D) (None, 7, 7, 160) 153600 block_15_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_15_project_BN (BatchNorma (None, 7, 7, 160) 640 block_15_project[0][0]
__________________________________________________________________________________________________
block_15_add (Add) (None, 7, 7, 160) 0 block_14_add[0][0]
block_15_project_BN[0][0]
__________________________________________________________________________________________________
block_16_expand (Conv2D) (None, 7, 7, 960) 153600 block_15_add[0][0]
__________________________________________________________________________________________________
block_16_expand_BN (BatchNormal (None, 7, 7, 960) 3840 block_16_expand[0][0]
__________________________________________________________________________________________________
block_16_expand_relu (ReLU) (None, 7, 7, 960) 0 block_16_expand_BN[0][0]
__________________________________________________________________________________________________
block_16_depthwise (DepthwiseCo (None, 7, 7, 960) 8640 block_16_expand_relu[0][0]
__________________________________________________________________________________________________
block_16_depthwise_BN (BatchNor (None, 7, 7, 960) 3840 block_16_depthwise[0][0]
__________________________________________________________________________________________________
block_16_depthwise_relu (ReLU) (None, 7, 7, 960) 0 block_16_depthwise_BN[0][0]
__________________________________________________________________________________________________
block_16_project (Conv2D) (None, 7, 7, 320) 307200 block_16_depthwise_relu[0][0]
__________________________________________________________________________________________________
block_16_project_BN (BatchNorma (None, 7, 7, 320) 1280 block_16_project[0][0]
__________________________________________________________________________________________________
Conv_1 (Conv2D) (None, 7, 7, 1280) 409600 block_16_project_BN[0][0]
__________________________________________________________________________________________________
Conv_1_bn (BatchNormalization) (None, 7, 7, 1280) 5120 Conv_1[0][0]
__________________________________________________________________________________________________
out_relu (ReLU) (None, 7, 7, 1280) 0 Conv_1_bn[0][0]
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 1280) 0 out_relu[0][0]
==================================================================================================
Total params: 2,257,984
Trainable params: 2,223,872
Non-trainable params: 34,112
__________________________________________________________________________________________________
None
(None, 1280)
In [10]:
from keras.utils import to_categorical
def test_train_dev_split(input_data, output_data, train=0.8, dev=0.1,
test=0.1):
#make seed for exact results everything
#input_data=preprocess_input(input_data)
input_data, output_data = shuffle(input_data, output_data, random_state=0)
split1 = int(train * len(input_data))
split2 = int((train + dev) * len(input_data))
train_input = input_data[:split1]
dev_input = input_data[split1:split2]
test_input = input_data[split2:]
train_output = output_data[:split1]
dev_output = output_data[split1:split2]
test_output = output_data[split2:]
train_input=model2.predict(preprocess_input(np.array([np.array(i.resize((224,224))) for i in train_input])))
dev_input=model2.predict(preprocess_input(np.array([np.array(i.resize((224,224))) for i in dev_input])))
test_input=model2.predict(preprocess_input(np.array([np.array(i.resize((224,224))) for i in test_input])))
print(train_input[0])
return train_input,np.array(train_output),dev_input, np.array(dev_output),test_input, np.array(test_output)
In [11]:
from keras import backend as K
def euc_dist_keras(y_true, y_pred):
return K.sqrt(K.sum(K.square(y_true - y_pred), axis=-1, keepdims=True))
In [12]:
def phone_finder_model_dnn(input_data, output_data):
train_in, train_out, dev_in, dev_out, test_in, test_out = test_train_dev_split(
input_data, output_data)
#https://arxiv.org/pdf/1509.05371v2.pdf
print(np.array(train_in).shape)
model=Sequential()
model.add(Dense(64,activation='relu',input_shape=(1280,)))
model.add(Dropout(0.4))
'''model.add(Dense(256,activation='relu'))
model.add(Dropout(0.4))
model.add(BatchNormalization())
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.4))'''
model.add(Dense(8,activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(2,activation='linear'))
print(model.input_shape,model.output_shape)
print(model.summary())
'''
model.compile(
optimizer=adam(0.001),
loss=[focal_loss(alpha=.25, gamma=2)],
metrics=['accuracy'])
'''
model.compile(
optimizer=adam(0.0001),
loss='mse',
metrics=['mae'])
early = EarlyStopping(patience=100)
check = ModelCheckpoint(
'find_phone_dnn_model.h5',
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
model_history=model.fit(
train_in,
train_out,
batch_size=32,
callbacks=[early, check],
validation_data=(dev_in, dev_out),
epochs=5000)
loss,mse = model.evaluate(test_in, test_out)
print("Loss: {0} MAE: {1}".format(loss, mse))
plt.plot(model_history.history['loss'])
plt.plot(model_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
return model
dnn_model=phone_finder_model_dnn(input_data,labels)
[0.9329858 0.02124948 0.06839955 ... 0.649957 0.3779843 0.26595476] (102, 1280) WARNING:tensorflow:From C:\Users\Ravi\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. (None, 1280) (None, 2) _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 64) 81984 _________________________________________________________________ dropout_1 (Dropout) (None, 64) 0 _________________________________________________________________ dense_2 (Dense) (None, 8) 520 _________________________________________________________________ dropout_2 (Dropout) (None, 8) 0 _________________________________________________________________ dense_3 (Dense) (None, 2) 18 ================================================================= Total params: 82,522 Trainable params: 82,522 Non-trainable params: 0 _________________________________________________________________ None WARNING:tensorflow:From C:\Users\Ravi\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. Train on 102 samples, validate on 13 samples Epoch 1/5000 102/102 [==============================] - ETA: 2s - loss: 1.9813 - mean_absolute_error: 1.037 - 1s 11ms/step - loss: 2.0316 - mean_absolute_error: 1.0411 - val_loss: 0.3749 - val_mean_absolute_error: 0.4678 Epoch 00001: val_loss improved from inf to 0.37492, saving model to find_phone_dnn_model.h5 Epoch 2/5000 102/102 [==============================] - ETA: 0s - loss: 1.2594 - mean_absolute_error: 0.779 - 0s 225us/step - loss: 1.0635 - mean_absolute_error: 0.7977 - val_loss: 0.1786 - val_mean_absolute_error: 0.3445 Epoch 00002: val_loss improved from 0.37492 to 0.17863, saving model to find_phone_dnn_model.h5 Epoch 3/5000 102/102 [==============================] - ETA: 0s - loss: 0.7726 - mean_absolute_error: 0.687 - 0s 196us/step - loss: 0.6690 - mean_absolute_error: 0.6398 - val_loss: 0.1362 - val_mean_absolute_error: 0.2936 Epoch 00003: val_loss improved from 0.17863 to 0.13623, saving model to find_phone_dnn_model.h5 Epoch 4/5000 102/102 [==============================] - ETA: 0s - loss: 0.7700 - mean_absolute_error: 0.709 - 0s 225us/step - loss: 0.6388 - mean_absolute_error: 0.6333 - val_loss: 0.1323 - val_mean_absolute_error: 0.2936 Epoch 00004: val_loss improved from 0.13623 to 0.13229, saving model to find_phone_dnn_model.h5 Epoch 5/5000 102/102 [==============================] - ETA: 0s - loss: 0.3698 - mean_absolute_error: 0.506 - 0s 264us/step - loss: 0.4278 - mean_absolute_error: 0.5194 - val_loss: 0.1334 - val_mean_absolute_error: 0.2998 Epoch 00005: val_loss did not improve from 0.13229 Epoch 6/5000 102/102 [==============================] - ETA: 0s - loss: 0.6113 - mean_absolute_error: 0.640 - 0s 225us/step - loss: 0.4552 - mean_absolute_error: 0.5437 - val_loss: 0.1348 - val_mean_absolute_error: 0.3032 Epoch 00006: val_loss did not improve from 0.13229 Epoch 7/5000 102/102 [==============================] - ETA: 0s - loss: 0.2812 - mean_absolute_error: 0.443 - 0s 254us/step - loss: 0.4097 - mean_absolute_error: 0.5149 - val_loss: 0.1369 - val_mean_absolute_error: 0.3048 Epoch 00007: val_loss did not improve from 0.13229 Epoch 8/5000 102/102 [==============================] - ETA: 0s - loss: 0.3629 - mean_absolute_error: 0.523 - 0s 254us/step - loss: 0.3474 - mean_absolute_error: 0.4921 - val_loss: 0.1405 - val_mean_absolute_error: 0.3074 Epoch 00008: val_loss did not improve from 0.13229 Epoch 9/5000 102/102 [==============================] - ETA: 0s - loss: 0.2477 - mean_absolute_error: 0.403 - 0s 235us/step - loss: 0.2707 - mean_absolute_error: 0.4309 - val_loss: 0.1430 - val_mean_absolute_error: 0.3082 Epoch 00009: val_loss did not improve from 0.13229 Epoch 10/5000 102/102 [==============================] - ETA: 0s - loss: 0.2561 - mean_absolute_error: 0.417 - 0s 244us/step - loss: 0.2894 - mean_absolute_error: 0.4454 - val_loss: 0.1468 - val_mean_absolute_error: 0.3102 Epoch 00010: val_loss did not improve from 0.13229 Epoch 11/5000 102/102 [==============================] - ETA: 0s - loss: 0.2658 - mean_absolute_error: 0.442 - 0s 205us/step - loss: 0.2855 - mean_absolute_error: 0.4425 - val_loss: 0.1512 - val_mean_absolute_error: 0.3143 Epoch 00011: val_loss did not improve from 0.13229 Epoch 12/5000 102/102 [==============================] - ETA: 0s - loss: 0.3395 - mean_absolute_error: 0.496 - 0s 196us/step - loss: 0.3062 - mean_absolute_error: 0.4627 - val_loss: 0.1557 - val_mean_absolute_error: 0.3180 Epoch 00012: val_loss did not improve from 0.13229 Epoch 13/5000 102/102 [==============================] - ETA: 0s - loss: 0.2317 - mean_absolute_error: 0.411 - 0s 225us/step - loss: 0.3019 - mean_absolute_error: 0.4520 - val_loss: 0.1581 - val_mean_absolute_error: 0.3212 Epoch 00013: val_loss did not improve from 0.13229 Epoch 14/5000 102/102 [==============================] - ETA: 0s - loss: 0.2776 - mean_absolute_error: 0.430 - 0s 205us/step - loss: 0.2530 - mean_absolute_error: 0.4171 - val_loss: 0.1591 - val_mean_absolute_error: 0.3242 Epoch 00014: val_loss did not improve from 0.13229 Epoch 15/5000 102/102 [==============================] - ETA: 0s - loss: 0.1936 - mean_absolute_error: 0.358 - 0s 225us/step - loss: 0.2698 - mean_absolute_error: 0.4236 - val_loss: 0.1583 - val_mean_absolute_error: 0.3240 Epoch 00015: val_loss did not improve from 0.13229 Epoch 16/5000 102/102 [==============================] - ETA: 0s - loss: 0.2333 - mean_absolute_error: 0.424 - 0s 215us/step - loss: 0.2837 - mean_absolute_error: 0.4432 - val_loss: 0.1589 - val_mean_absolute_error: 0.3246 Epoch 00016: val_loss did not improve from 0.13229 Epoch 17/5000 102/102 [==============================] - ETA: 0s - loss: 0.1914 - mean_absolute_error: 0.367 - 0s 215us/step - loss: 0.2331 - mean_absolute_error: 0.4065 - val_loss: 0.1619 - val_mean_absolute_error: 0.3283 Epoch 00017: val_loss did not improve from 0.13229 Epoch 18/5000 102/102 [==============================] - ETA: 0s - loss: 0.2536 - mean_absolute_error: 0.410 - 0s 244us/step - loss: 0.2431 - mean_absolute_error: 0.4206 - val_loss: 0.1617 - val_mean_absolute_error: 0.3278 Epoch 00018: val_loss did not improve from 0.13229 Epoch 19/5000 102/102 [==============================] - ETA: 0s - loss: 0.2487 - mean_absolute_error: 0.420 - 0s 215us/step - loss: 0.2402 - mean_absolute_error: 0.4136 - val_loss: 0.1595 - val_mean_absolute_error: 0.3247 Epoch 00019: val_loss did not improve from 0.13229 Epoch 20/5000 102/102 [==============================] - ETA: 0s - loss: 0.2135 - mean_absolute_error: 0.397 - 0s 166us/step - loss: 0.2358 - mean_absolute_error: 0.4071 - val_loss: 0.1553 - val_mean_absolute_error: 0.3185 Epoch 00020: val_loss did not improve from 0.13229 Epoch 21/5000 102/102 [==============================] - ETA: 0s - loss: 0.2595 - mean_absolute_error: 0.431 - 0s 225us/step - loss: 0.2367 - mean_absolute_error: 0.4133 - val_loss: 0.1522 - val_mean_absolute_error: 0.3137 Epoch 00021: val_loss did not improve from 0.13229 Epoch 22/5000 102/102 [==============================] - ETA: 0s - loss: 0.2406 - mean_absolute_error: 0.409 - 0s 215us/step - loss: 0.2805 - mean_absolute_error: 0.4489 - val_loss: 0.1498 - val_mean_absolute_error: 0.3099 Epoch 00022: val_loss did not improve from 0.13229 Epoch 23/5000 102/102 [==============================] - ETA: 0s - loss: 0.2798 - mean_absolute_error: 0.442 - 0s 205us/step - loss: 0.2423 - mean_absolute_error: 0.4175 - val_loss: 0.1491 - val_mean_absolute_error: 0.3088 Epoch 00023: val_loss did not improve from 0.13229 Epoch 24/5000 102/102 [==============================] - ETA: 0s - loss: 0.2854 - mean_absolute_error: 0.448 - 0s 215us/step - loss: 0.2600 - mean_absolute_error: 0.4256 - val_loss: 0.1505 - val_mean_absolute_error: 0.3112 Epoch 00024: val_loss did not improve from 0.13229 Epoch 25/5000 102/102 [==============================] - ETA: 0s - loss: 0.1917 - mean_absolute_error: 0.365 - 0s 215us/step - loss: 0.2347 - mean_absolute_error: 0.4100 - val_loss: 0.1516 - val_mean_absolute_error: 0.3128 Epoch 00025: val_loss did not improve from 0.13229 Epoch 26/5000 102/102 [==============================] - ETA: 0s - loss: 0.2365 - mean_absolute_error: 0.403 - 0s 244us/step - loss: 0.2368 - mean_absolute_error: 0.4135 - val_loss: 0.1547 - val_mean_absolute_error: 0.3172 Epoch 00026: val_loss did not improve from 0.13229 Epoch 27/5000 102/102 [==============================] - ETA: 0s - loss: 0.2558 - mean_absolute_error: 0.432 - 0s 205us/step - loss: 0.2453 - mean_absolute_error: 0.4262 - val_loss: 0.1579 - val_mean_absolute_error: 0.3215 Epoch 00027: val_loss did not improve from 0.13229 Epoch 28/5000 102/102 [==============================] - ETA: 0s - loss: 0.2546 - mean_absolute_error: 0.432 - 0s 205us/step - loss: 0.2727 - mean_absolute_error: 0.4352 - val_loss: 0.1607 - val_mean_absolute_error: 0.3250 Epoch 00028: val_loss did not improve from 0.13229 Epoch 29/5000 102/102 [==============================] - ETA: 0s - loss: 0.2521 - mean_absolute_error: 0.427 - 0s 225us/step - loss: 0.2710 - mean_absolute_error: 0.4398 - val_loss: 0.1602 - val_mean_absolute_error: 0.3240 Epoch 00029: val_loss did not improve from 0.13229 Epoch 30/5000 102/102 [==============================] - ETA: 0s - loss: 0.2245 - mean_absolute_error: 0.403 - 0s 215us/step - loss: 0.2422 - mean_absolute_error: 0.4206 - val_loss: 0.1569 - val_mean_absolute_error: 0.3190 Epoch 00030: val_loss did not improve from 0.13229 Epoch 31/5000 102/102 [==============================] - ETA: 0s - loss: 0.3420 - mean_absolute_error: 0.473 - 0s 196us/step - loss: 0.2537 - mean_absolute_error: 0.4165 - val_loss: 0.1540 - val_mean_absolute_error: 0.3142 Epoch 00031: val_loss did not improve from 0.13229 Epoch 32/5000 102/102 [==============================] - ETA: 0s - loss: 0.1911 - mean_absolute_error: 0.349 - 0s 215us/step - loss: 0.2328 - mean_absolute_error: 0.3993 - val_loss: 0.1519 - val_mean_absolute_error: 0.3108 Epoch 00032: val_loss did not improve from 0.13229 Epoch 33/5000 102/102 [==============================] - ETA: 0s - loss: 0.2076 - mean_absolute_error: 0.392 - 0s 205us/step - loss: 0.2222 - mean_absolute_error: 0.4100 - val_loss: 0.1482 - val_mean_absolute_error: 0.3059 Epoch 00033: val_loss did not improve from 0.13229 Epoch 34/5000 102/102 [==============================] - ETA: 0s - loss: 0.2631 - mean_absolute_error: 0.439 - 0s 225us/step - loss: 0.2456 - mean_absolute_error: 0.4145 - val_loss: 0.1427 - val_mean_absolute_error: 0.2982 Epoch 00034: val_loss did not improve from 0.13229 Epoch 35/5000 102/102 [==============================] - ETA: 0s - loss: 0.2280 - mean_absolute_error: 0.405 - 0s 205us/step - loss: 0.2270 - mean_absolute_error: 0.4051 - val_loss: 0.1387 - val_mean_absolute_error: 0.2920 Epoch 00035: val_loss did not improve from 0.13229 Epoch 36/5000 102/102 [==============================] - ETA: 0s - loss: 0.2318 - mean_absolute_error: 0.411 - 0s 196us/step - loss: 0.2311 - mean_absolute_error: 0.4094 - val_loss: 0.1341 - val_mean_absolute_error: 0.2840 Epoch 00036: val_loss did not improve from 0.13229 Epoch 37/5000 102/102 [==============================] - ETA: 0s - loss: 0.1681 - mean_absolute_error: 0.340 - 0s 225us/step - loss: 0.2312 - mean_absolute_error: 0.3997 - val_loss: 0.1310 - val_mean_absolute_error: 0.2786 Epoch 00037: val_loss improved from 0.13229 to 0.13104, saving model to find_phone_dnn_model.h5 Epoch 38/5000 102/102 [==============================] - ETA: 0s - loss: 0.2252 - mean_absolute_error: 0.395 - 0s 205us/step - loss: 0.2335 - mean_absolute_error: 0.4064 - val_loss: 0.1313 - val_mean_absolute_error: 0.2794 Epoch 00038: val_loss did not improve from 0.13104 Epoch 39/5000 102/102 [==============================] - ETA: 0s - loss: 0.2070 - mean_absolute_error: 0.366 - 0s 215us/step - loss: 0.2172 - mean_absolute_error: 0.3921 - val_loss: 0.1334 - val_mean_absolute_error: 0.2836 Epoch 00039: val_loss did not improve from 0.13104 Epoch 40/5000 102/102 [==============================] - ETA: 0s - loss: 0.2361 - mean_absolute_error: 0.416 - 0s 205us/step - loss: 0.1982 - mean_absolute_error: 0.3760 - val_loss: 0.1347 - val_mean_absolute_error: 0.2861 Epoch 00040: val_loss did not improve from 0.13104 Epoch 41/5000 102/102 [==============================] - ETA: 0s - loss: 0.2445 - mean_absolute_error: 0.414 - 0s 215us/step - loss: 0.2298 - mean_absolute_error: 0.3974 - val_loss: 0.1353 - val_mean_absolute_error: 0.2873 Epoch 00041: val_loss did not improve from 0.13104 Epoch 42/5000 102/102 [==============================] - ETA: 0s - loss: 0.2764 - mean_absolute_error: 0.430 - 0s 186us/step - loss: 0.2291 - mean_absolute_error: 0.4008 - val_loss: 0.1365 - val_mean_absolute_error: 0.2893 Epoch 00042: val_loss did not improve from 0.13104 Epoch 43/5000 102/102 [==============================] - ETA: 0s - loss: 0.2352 - mean_absolute_error: 0.412 - 0s 205us/step - loss: 0.2239 - mean_absolute_error: 0.3921 - val_loss: 0.1390 - val_mean_absolute_error: 0.2935 Epoch 00043: val_loss did not improve from 0.13104 Epoch 44/5000 102/102 [==============================] - ETA: 0s - loss: 0.1643 - mean_absolute_error: 0.343 - 0s 196us/step - loss: 0.2145 - mean_absolute_error: 0.3870 - val_loss: 0.1425 - val_mean_absolute_error: 0.3000 Epoch 00044: val_loss did not improve from 0.13104 Epoch 45/5000 102/102 [==============================] - ETA: 0s - loss: 0.2126 - mean_absolute_error: 0.390 - 0s 225us/step - loss: 0.2122 - mean_absolute_error: 0.3880 - val_loss: 0.1437 - val_mean_absolute_error: 0.3028 Epoch 00045: val_loss did not improve from 0.13104 Epoch 46/5000 102/102 [==============================] - ETA: 0s - loss: 0.2726 - mean_absolute_error: 0.439 - 0s 215us/step - loss: 0.2127 - mean_absolute_error: 0.3875 - val_loss: 0.1458 - val_mean_absolute_error: 0.3068 Epoch 00046: val_loss did not improve from 0.13104 Epoch 47/5000 102/102 [==============================] - ETA: 0s - loss: 0.2447 - mean_absolute_error: 0.408 - 0s 205us/step - loss: 0.2183 - mean_absolute_error: 0.3973 - val_loss: 0.1456 - val_mean_absolute_error: 0.3068 Epoch 00047: val_loss did not improve from 0.13104 Epoch 48/5000 102/102 [==============================] - ETA: 0s - loss: 0.2120 - mean_absolute_error: 0.380 - 0s 205us/step - loss: 0.2222 - mean_absolute_error: 0.3971 - val_loss: 0.1442 - val_mean_absolute_error: 0.3046 Epoch 00048: val_loss did not improve from 0.13104 Epoch 49/5000 102/102 [==============================] - ETA: 0s - loss: 0.2648 - mean_absolute_error: 0.453 - 0s 215us/step - loss: 0.2400 - mean_absolute_error: 0.4150 - val_loss: 0.1437 - val_mean_absolute_error: 0.3034 Epoch 00049: val_loss did not improve from 0.13104 Epoch 50/5000 102/102 [==============================] - ETA: 0s - loss: 0.2319 - mean_absolute_error: 0.400 - 0s 225us/step - loss: 0.2328 - mean_absolute_error: 0.3975 - val_loss: 0.1428 - val_mean_absolute_error: 0.3015 Epoch 00050: val_loss did not improve from 0.13104 Epoch 51/5000 102/102 [==============================] - ETA: 0s - loss: 0.2001 - mean_absolute_error: 0.369 - 0s 215us/step - loss: 0.1851 - mean_absolute_error: 0.3589 - val_loss: 0.1416 - val_mean_absolute_error: 0.2995 Epoch 00051: val_loss did not improve from 0.13104 Epoch 52/5000 102/102 [==============================] - ETA: 0s - loss: 0.1928 - mean_absolute_error: 0.361 - 0s 205us/step - loss: 0.2016 - mean_absolute_error: 0.3709 - val_loss: 0.1400 - val_mean_absolute_error: 0.2970 Epoch 00052: val_loss did not improve from 0.13104 Epoch 53/5000 102/102 [==============================] - ETA: 0s - loss: 0.2071 - mean_absolute_error: 0.385 - 0s 215us/step - loss: 0.1858 - mean_absolute_error: 0.3600 - val_loss: 0.1370 - val_mean_absolute_error: 0.2918 Epoch 00053: val_loss did not improve from 0.13104 Epoch 54/5000 102/102 [==============================] - ETA: 0s - loss: 0.2595 - mean_absolute_error: 0.408 - 0s 225us/step - loss: 0.2170 - mean_absolute_error: 0.3823 - val_loss: 0.1319 - val_mean_absolute_error: 0.2840 Epoch 00054: val_loss did not improve from 0.13104 Epoch 55/5000 102/102 [==============================] - ETA: 0s - loss: 0.2014 - mean_absolute_error: 0.348 - 0s 205us/step - loss: 0.1927 - mean_absolute_error: 0.3562 - val_loss: 0.1276 - val_mean_absolute_error: 0.2781 Epoch 00055: val_loss improved from 0.13104 to 0.12761, saving model to find_phone_dnn_model.h5 Epoch 56/5000 102/102 [==============================] - ETA: 0s - loss: 0.2377 - mean_absolute_error: 0.410 - 0s 205us/step - loss: 0.2169 - mean_absolute_error: 0.3898 - val_loss: 0.1253 - val_mean_absolute_error: 0.2739 Epoch 00056: val_loss improved from 0.12761 to 0.12535, saving model to find_phone_dnn_model.h5 Epoch 57/5000 102/102 [==============================] - ETA: 0s - loss: 0.2361 - mean_absolute_error: 0.400 - 0s 205us/step - loss: 0.2090 - mean_absolute_error: 0.3793 - val_loss: 0.1259 - val_mean_absolute_error: 0.2740 Epoch 00057: val_loss did not improve from 0.12535 Epoch 58/5000 102/102 [==============================] - ETA: 0s - loss: 0.1768 - mean_absolute_error: 0.350 - 0s 195us/step - loss: 0.1969 - mean_absolute_error: 0.3740 - val_loss: 0.1295 - val_mean_absolute_error: 0.2786 Epoch 00058: val_loss did not improve from 0.12535 Epoch 59/5000 102/102 [==============================] - ETA: 0s - loss: 0.1965 - mean_absolute_error: 0.373 - 0s 254us/step - loss: 0.2186 - mean_absolute_error: 0.3982 - val_loss: 0.1352 - val_mean_absolute_error: 0.2866 Epoch 00059: val_loss did not improve from 0.12535 Epoch 60/5000 102/102 [==============================] - ETA: 0s - loss: 0.1976 - mean_absolute_error: 0.395 - 0s 264us/step - loss: 0.2130 - mean_absolute_error: 0.3954 - val_loss: 0.1410 - val_mean_absolute_error: 0.2943 Epoch 00060: val_loss did not improve from 0.12535 Epoch 61/5000 102/102 [==============================] - ETA: 0s - loss: 0.1690 - mean_absolute_error: 0.352 - 0s 235us/step - loss: 0.2113 - mean_absolute_error: 0.3838 - val_loss: 0.1457 - val_mean_absolute_error: 0.3005 Epoch 00061: val_loss did not improve from 0.12535 Epoch 62/5000 102/102 [==============================] - ETA: 0s - loss: 0.1759 - mean_absolute_error: 0.337 - 0s 274us/step - loss: 0.2044 - mean_absolute_error: 0.3669 - val_loss: 0.1470 - val_mean_absolute_error: 0.3023 Epoch 00062: val_loss did not improve from 0.12535 Epoch 63/5000 102/102 [==============================] - ETA: 0s - loss: 0.1660 - mean_absolute_error: 0.334 - 0s 225us/step - loss: 0.1797 - mean_absolute_error: 0.3575 - val_loss: 0.1470 - val_mean_absolute_error: 0.3024 Epoch 00063: val_loss did not improve from 0.12535 Epoch 64/5000 102/102 [==============================] - ETA: 0s - loss: 0.2371 - mean_absolute_error: 0.428 - 0s 225us/step - loss: 0.2110 - mean_absolute_error: 0.3835 - val_loss: 0.1460 - val_mean_absolute_error: 0.3012 Epoch 00064: val_loss did not improve from 0.12535 Epoch 65/5000 102/102 [==============================] - ETA: 0s - loss: 0.2313 - mean_absolute_error: 0.395 - 0s 225us/step - loss: 0.1977 - mean_absolute_error: 0.3631 - val_loss: 0.1450 - val_mean_absolute_error: 0.2996 Epoch 00065: val_loss did not improve from 0.12535 Epoch 66/5000 102/102 [==============================] - ETA: 0s - loss: 0.1675 - mean_absolute_error: 0.319 - 0s 196us/step - loss: 0.1670 - mean_absolute_error: 0.3239 - val_loss: 0.1424 - val_mean_absolute_error: 0.2956 Epoch 00066: val_loss did not improve from 0.12535 Epoch 67/5000 102/102 [==============================] - ETA: 0s - loss: 0.1364 - mean_absolute_error: 0.299 - 0s 215us/step - loss: 0.1852 - mean_absolute_error: 0.3536 - val_loss: 0.1396 - val_mean_absolute_error: 0.2915 Epoch 00067: val_loss did not improve from 0.12535 Epoch 68/5000 102/102 [==============================] - ETA: 0s - loss: 0.1686 - mean_absolute_error: 0.348 - 0s 225us/step - loss: 0.1791 - mean_absolute_error: 0.3500 - val_loss: 0.1373 - val_mean_absolute_error: 0.2882 Epoch 00068: val_loss did not improve from 0.12535 Epoch 69/5000 102/102 [==============================] - ETA: 0s - loss: 0.2022 - mean_absolute_error: 0.376 - 0s 196us/step - loss: 0.1772 - mean_absolute_error: 0.3465 - val_loss: 0.1361 - val_mean_absolute_error: 0.2866 Epoch 00069: val_loss did not improve from 0.12535 Epoch 70/5000 102/102 [==============================] - ETA: 0s - loss: 0.1881 - mean_absolute_error: 0.360 - 0s 235us/step - loss: 0.1753 - mean_absolute_error: 0.3414 - val_loss: 0.1340 - val_mean_absolute_error: 0.2840 Epoch 00070: val_loss did not improve from 0.12535 Epoch 71/5000 102/102 [==============================] - ETA: 0s - loss: 0.1964 - mean_absolute_error: 0.335 - 0s 235us/step - loss: 0.1924 - mean_absolute_error: 0.3512 - val_loss: 0.1353 - val_mean_absolute_error: 0.2867 Epoch 00071: val_loss did not improve from 0.12535 Epoch 72/5000 102/102 [==============================] - ETA: 0s - loss: 0.1383 - mean_absolute_error: 0.294 - 0s 244us/step - loss: 0.1834 - mean_absolute_error: 0.3534 - val_loss: 0.1356 - val_mean_absolute_error: 0.2876 Epoch 00072: val_loss did not improve from 0.12535 Epoch 73/5000 102/102 [==============================] - ETA: 0s - loss: 0.2037 - mean_absolute_error: 0.387 - 0s 196us/step - loss: 0.1920 - mean_absolute_error: 0.3599 - val_loss: 0.1365 - val_mean_absolute_error: 0.2887 Epoch 00073: val_loss did not improve from 0.12535 Epoch 74/5000 102/102 [==============================] - ETA: 0s - loss: 0.1510 - mean_absolute_error: 0.327 - 0s 235us/step - loss: 0.1636 - mean_absolute_error: 0.3301 - val_loss: 0.1369 - val_mean_absolute_error: 0.2894 Epoch 00074: val_loss did not improve from 0.12535 Epoch 75/5000 102/102 [==============================] - ETA: 0s - loss: 0.2080 - mean_absolute_error: 0.380 - 0s 205us/step - loss: 0.1850 - mean_absolute_error: 0.3491 - val_loss: 0.1364 - val_mean_absolute_error: 0.2887 Epoch 00075: val_loss did not improve from 0.12535 Epoch 76/5000 102/102 [==============================] - ETA: 0s - loss: 0.1389 - mean_absolute_error: 0.311 - 0s 196us/step - loss: 0.1828 - mean_absolute_error: 0.3563 - val_loss: 0.1349 - val_mean_absolute_error: 0.2856 Epoch 00076: val_loss did not improve from 0.12535 Epoch 77/5000 102/102 [==============================] - ETA: 0s - loss: 0.2036 - mean_absolute_error: 0.371 - 0s 186us/step - loss: 0.1739 - mean_absolute_error: 0.3360 - val_loss: 0.1343 - val_mean_absolute_error: 0.2840 Epoch 00077: val_loss did not improve from 0.12535 Epoch 78/5000 102/102 [==============================] - ETA: 0s - loss: 0.2012 - mean_absolute_error: 0.375 - 0s 225us/step - loss: 0.1646 - mean_absolute_error: 0.3305 - val_loss: 0.1341 - val_mean_absolute_error: 0.2834 Epoch 00078: val_loss did not improve from 0.12535 Epoch 79/5000 102/102 [==============================] - ETA: 0s - loss: 0.1842 - mean_absolute_error: 0.331 - 0s 215us/step - loss: 0.1721 - mean_absolute_error: 0.3334 - val_loss: 0.1334 - val_mean_absolute_error: 0.2824 Epoch 00079: val_loss did not improve from 0.12535 Epoch 80/5000 102/102 [==============================] - ETA: 0s - loss: 0.1811 - mean_absolute_error: 0.347 - 0s 235us/step - loss: 0.1917 - mean_absolute_error: 0.3579 - val_loss: 0.1349 - val_mean_absolute_error: 0.2848 Epoch 00080: val_loss did not improve from 0.12535 Epoch 81/5000 102/102 [==============================] - ETA: 0s - loss: 0.1903 - mean_absolute_error: 0.379 - 0s 205us/step - loss: 0.1955 - mean_absolute_error: 0.3700 - val_loss: 0.1346 - val_mean_absolute_error: 0.2843 Epoch 00081: val_loss did not improve from 0.12535 Epoch 82/5000 102/102 [==============================] - ETA: 0s - loss: 0.1998 - mean_absolute_error: 0.376 - 0s 244us/step - loss: 0.1798 - mean_absolute_error: 0.3504 - val_loss: 0.1336 - val_mean_absolute_error: 0.2829 Epoch 00082: val_loss did not improve from 0.12535 Epoch 83/5000 102/102 [==============================] - ETA: 0s - loss: 0.1440 - mean_absolute_error: 0.305 - 0s 215us/step - loss: 0.1600 - mean_absolute_error: 0.3282 - val_loss: 0.1318 - val_mean_absolute_error: 0.2796 Epoch 00083: val_loss did not improve from 0.12535 Epoch 84/5000 102/102 [==============================] - ETA: 0s - loss: 0.2073 - mean_absolute_error: 0.379 - 0s 225us/step - loss: 0.1728 - mean_absolute_error: 0.3521 - val_loss: 0.1325 - val_mean_absolute_error: 0.2800 Epoch 00084: val_loss did not improve from 0.12535 Epoch 85/5000 102/102 [==============================] - ETA: 0s - loss: 0.1944 - mean_absolute_error: 0.362 - 0s 235us/step - loss: 0.1795 - mean_absolute_error: 0.3467 - val_loss: 0.1327 - val_mean_absolute_error: 0.2804 Epoch 00085: val_loss did not improve from 0.12535 Epoch 86/5000 102/102 [==============================] - ETA: 0s - loss: 0.1651 - mean_absolute_error: 0.313 - 0s 225us/step - loss: 0.1710 - mean_absolute_error: 0.3324 - val_loss: 0.1317 - val_mean_absolute_error: 0.2789 Epoch 00086: val_loss did not improve from 0.12535 Epoch 87/5000 102/102 [==============================] - ETA: 0s - loss: 0.1648 - mean_absolute_error: 0.348 - 0s 215us/step - loss: 0.1823 - mean_absolute_error: 0.3597 - val_loss: 0.1315 - val_mean_absolute_error: 0.2779 Epoch 00087: val_loss did not improve from 0.12535 Epoch 88/5000 102/102 [==============================] - ETA: 0s - loss: 0.1921 - mean_absolute_error: 0.360 - 0s 196us/step - loss: 0.1883 - mean_absolute_error: 0.3595 - val_loss: 0.1325 - val_mean_absolute_error: 0.2780 Epoch 00088: val_loss did not improve from 0.12535 Epoch 89/5000 102/102 [==============================] - ETA: 0s - loss: 0.1631 - mean_absolute_error: 0.337 - 0s 196us/step - loss: 0.1762 - mean_absolute_error: 0.3503 - val_loss: 0.1346 - val_mean_absolute_error: 0.2805 Epoch 00089: val_loss did not improve from 0.12535 Epoch 90/5000 102/102 [==============================] - ETA: 0s - loss: 0.1480 - mean_absolute_error: 0.316 - 0s 176us/step - loss: 0.1561 - mean_absolute_error: 0.3165 - val_loss: 0.1369 - val_mean_absolute_error: 0.2834 Epoch 00090: val_loss did not improve from 0.12535 Epoch 91/5000 102/102 [==============================] - ETA: 0s - loss: 0.1887 - mean_absolute_error: 0.365 - 0s 225us/step - loss: 0.1866 - mean_absolute_error: 0.3576 - val_loss: 0.1372 - val_mean_absolute_error: 0.2832 Epoch 00091: val_loss did not improve from 0.12535 Epoch 92/5000 102/102 [==============================] - ETA: 0s - loss: 0.2069 - mean_absolute_error: 0.348 - 0s 205us/step - loss: 0.1784 - mean_absolute_error: 0.3411 - val_loss: 0.1369 - val_mean_absolute_error: 0.2829 Epoch 00092: val_loss did not improve from 0.12535 Epoch 93/5000 102/102 [==============================] - ETA: 0s - loss: 0.2118 - mean_absolute_error: 0.389 - 0s 235us/step - loss: 0.1741 - mean_absolute_error: 0.3358 - val_loss: 0.1348 - val_mean_absolute_error: 0.2795 Epoch 00093: val_loss did not improve from 0.12535 Epoch 94/5000 102/102 [==============================] - ETA: 0s - loss: 0.1615 - mean_absolute_error: 0.296 - 0s 205us/step - loss: 0.1666 - mean_absolute_error: 0.3281 - val_loss: 0.1331 - val_mean_absolute_error: 0.2768 Epoch 00094: val_loss did not improve from 0.12535 Epoch 95/5000 102/102 [==============================] - ETA: 0s - loss: 0.1476 - mean_absolute_error: 0.314 - 0s 244us/step - loss: 0.1499 - mean_absolute_error: 0.3107 - val_loss: 0.1316 - val_mean_absolute_error: 0.2750 Epoch 00095: val_loss did not improve from 0.12535 Epoch 96/5000 102/102 [==============================] - ETA: 0s - loss: 0.1429 - mean_absolute_error: 0.314 - 0s 196us/step - loss: 0.1545 - mean_absolute_error: 0.3152 - val_loss: 0.1329 - val_mean_absolute_error: 0.2776 Epoch 00096: val_loss did not improve from 0.12535 Epoch 97/5000 102/102 [==============================] - ETA: 0s - loss: 0.1652 - mean_absolute_error: 0.316 - 0s 205us/step - loss: 0.1642 - mean_absolute_error: 0.3287 - val_loss: 0.1354 - val_mean_absolute_error: 0.2819 Epoch 00097: val_loss did not improve from 0.12535 Epoch 98/5000 102/102 [==============================] - ETA: 0s - loss: 0.1768 - mean_absolute_error: 0.355 - 0s 225us/step - loss: 0.1708 - mean_absolute_error: 0.3342 - val_loss: 0.1376 - val_mean_absolute_error: 0.2855 Epoch 00098: val_loss did not improve from 0.12535 Epoch 99/5000 102/102 [==============================] - ETA: 0s - loss: 0.1938 - mean_absolute_error: 0.366 - 0s 205us/step - loss: 0.1893 - mean_absolute_error: 0.3568 - val_loss: 0.1382 - val_mean_absolute_error: 0.2866 Epoch 00099: val_loss did not improve from 0.12535 Epoch 100/5000 102/102 [==============================] - ETA: 0s - loss: 0.1553 - mean_absolute_error: 0.321 - 0s 244us/step - loss: 0.1622 - mean_absolute_error: 0.3261 - val_loss: 0.1371 - val_mean_absolute_error: 0.2854 Epoch 00100: val_loss did not improve from 0.12535 Epoch 101/5000 102/102 [==============================] - ETA: 0s - loss: 0.2072 - mean_absolute_error: 0.380 - 0s 235us/step - loss: 0.1598 - mean_absolute_error: 0.3324 - val_loss: 0.1346 - val_mean_absolute_error: 0.2821 Epoch 00101: val_loss did not improve from 0.12535 Epoch 102/5000 102/102 [==============================] - ETA: 0s - loss: 0.2149 - mean_absolute_error: 0.375 - 0s 196us/step - loss: 0.1702 - mean_absolute_error: 0.3346 - val_loss: 0.1304 - val_mean_absolute_error: 0.2758 Epoch 00102: val_loss did not improve from 0.12535 Epoch 103/5000 102/102 [==============================] - ETA: 0s - loss: 0.1429 - mean_absolute_error: 0.309 - 0s 225us/step - loss: 0.1409 - mean_absolute_error: 0.2938 - val_loss: 0.1270 - val_mean_absolute_error: 0.2711 Epoch 00103: val_loss did not improve from 0.12535 Epoch 104/5000 102/102 [==============================] - ETA: 0s - loss: 0.1833 - mean_absolute_error: 0.337 - 0s 196us/step - loss: 0.1522 - mean_absolute_error: 0.3145 - val_loss: 0.1231 - val_mean_absolute_error: 0.2655 Epoch 00104: val_loss improved from 0.12535 to 0.12313, saving model to find_phone_dnn_model.h5 Epoch 105/5000 102/102 [==============================] - ETA: 0s - loss: 0.1458 - mean_absolute_error: 0.305 - 0s 186us/step - loss: 0.1477 - mean_absolute_error: 0.3079 - val_loss: 0.1201 - val_mean_absolute_error: 0.2620 Epoch 00105: val_loss improved from 0.12313 to 0.12007, saving model to find_phone_dnn_model.h5 Epoch 106/5000 102/102 [==============================] - ETA: 0s - loss: 0.1250 - mean_absolute_error: 0.278 - 0s 186us/step - loss: 0.1434 - mean_absolute_error: 0.3037 - val_loss: 0.1201 - val_mean_absolute_error: 0.2617 Epoch 00106: val_loss improved from 0.12007 to 0.12006, saving model to find_phone_dnn_model.h5 Epoch 107/5000 102/102 [==============================] - ETA: 0s - loss: 0.1393 - mean_absolute_error: 0.299 - 0s 196us/step - loss: 0.1526 - mean_absolute_error: 0.3171 - val_loss: 0.1210 - val_mean_absolute_error: 0.2627 Epoch 00107: val_loss did not improve from 0.12006 Epoch 108/5000 102/102 [==============================] - ETA: 0s - loss: 0.1221 - mean_absolute_error: 0.284 - 0s 196us/step - loss: 0.1315 - mean_absolute_error: 0.2942 - val_loss: 0.1195 - val_mean_absolute_error: 0.2604 Epoch 00108: val_loss improved from 0.12006 to 0.11953, saving model to find_phone_dnn_model.h5 Epoch 109/5000 102/102 [==============================] - ETA: 0s - loss: 0.1721 - mean_absolute_error: 0.343 - 0s 215us/step - loss: 0.1723 - mean_absolute_error: 0.3383 - val_loss: 0.1188 - val_mean_absolute_error: 0.2594 Epoch 00109: val_loss improved from 0.11953 to 0.11885, saving model to find_phone_dnn_model.h5 Epoch 110/5000 102/102 [==============================] - ETA: 0s - loss: 0.1692 - mean_absolute_error: 0.337 - 0s 205us/step - loss: 0.1805 - mean_absolute_error: 0.3422 - val_loss: 0.1189 - val_mean_absolute_error: 0.2603 Epoch 00110: val_loss did not improve from 0.11885 Epoch 111/5000 102/102 [==============================] - ETA: 0s - loss: 0.1649 - mean_absolute_error: 0.313 - 0s 205us/step - loss: 0.1660 - mean_absolute_error: 0.3258 - val_loss: 0.1195 - val_mean_absolute_error: 0.2616 Epoch 00111: val_loss did not improve from 0.11885 Epoch 112/5000 102/102 [==============================] - ETA: 0s - loss: 0.1726 - mean_absolute_error: 0.344 - 0s 196us/step - loss: 0.1493 - mean_absolute_error: 0.3151 - val_loss: 0.1190 - val_mean_absolute_error: 0.2604 Epoch 00112: val_loss did not improve from 0.11885 Epoch 113/5000 102/102 [==============================] - ETA: 0s - loss: 0.1033 - mean_absolute_error: 0.260 - 0s 206us/step - loss: 0.1348 - mean_absolute_error: 0.2933 - val_loss: 0.1164 - val_mean_absolute_error: 0.2570 Epoch 00113: val_loss improved from 0.11885 to 0.11643, saving model to find_phone_dnn_model.h5 Epoch 114/5000 102/102 [==============================] - ETA: 0s - loss: 0.1308 - mean_absolute_error: 0.300 - 0s 185us/step - loss: 0.1483 - mean_absolute_error: 0.3121 - val_loss: 0.1123 - val_mean_absolute_error: 0.2514 Epoch 00114: val_loss improved from 0.11643 to 0.11228, saving model to find_phone_dnn_model.h5 Epoch 115/5000 102/102 [==============================] - ETA: 0s - loss: 0.1483 - mean_absolute_error: 0.310 - 0s 185us/step - loss: 0.1394 - mean_absolute_error: 0.2996 - val_loss: 0.1094 - val_mean_absolute_error: 0.2467 Epoch 00115: val_loss improved from 0.11228 to 0.10936, saving model to find_phone_dnn_model.h5 Epoch 116/5000 102/102 [==============================] - ETA: 0s - loss: 0.1542 - mean_absolute_error: 0.325 - 0s 196us/step - loss: 0.1343 - mean_absolute_error: 0.2981 - val_loss: 0.1072 - val_mean_absolute_error: 0.2430 Epoch 00116: val_loss improved from 0.10936 to 0.10721, saving model to find_phone_dnn_model.h5 Epoch 117/5000 102/102 [==============================] - ETA: 0s - loss: 0.1476 - mean_absolute_error: 0.314 - 0s 186us/step - loss: 0.1446 - mean_absolute_error: 0.3043 - val_loss: 0.1069 - val_mean_absolute_error: 0.2425 Epoch 00117: val_loss improved from 0.10721 to 0.10685, saving model to find_phone_dnn_model.h5 Epoch 118/5000 102/102 [==============================] - ETA: 0s - loss: 0.1500 - mean_absolute_error: 0.304 - 0s 196us/step - loss: 0.1644 - mean_absolute_error: 0.3196 - val_loss: 0.1081 - val_mean_absolute_error: 0.2445 Epoch 00118: val_loss did not improve from 0.10685 Epoch 119/5000 102/102 [==============================] - ETA: 0s - loss: 0.1502 - mean_absolute_error: 0.318 - 0s 205us/step - loss: 0.1366 - mean_absolute_error: 0.3037 - val_loss: 0.1102 - val_mean_absolute_error: 0.2477 Epoch 00119: val_loss did not improve from 0.10685 Epoch 120/5000 102/102 [==============================] - ETA: 0s - loss: 0.1464 - mean_absolute_error: 0.301 - 0s 186us/step - loss: 0.1449 - mean_absolute_error: 0.3040 - val_loss: 0.1090 - val_mean_absolute_error: 0.2476 Epoch 00120: val_loss did not improve from 0.10685 Epoch 121/5000 102/102 [==============================] - ETA: 0s - loss: 0.1819 - mean_absolute_error: 0.365 - 0s 196us/step - loss: 0.1437 - mean_absolute_error: 0.3138 - val_loss: 0.1102 - val_mean_absolute_error: 0.2497 Epoch 00121: val_loss did not improve from 0.10685 Epoch 122/5000 102/102 [==============================] - ETA: 0s - loss: 0.1234 - mean_absolute_error: 0.280 - 0s 215us/step - loss: 0.1400 - mean_absolute_error: 0.2985 - val_loss: 0.1135 - val_mean_absolute_error: 0.2531 Epoch 00122: val_loss did not improve from 0.10685 Epoch 123/5000 102/102 [==============================] - ETA: 0s - loss: 0.1022 - mean_absolute_error: 0.245 - 0s 215us/step - loss: 0.1254 - mean_absolute_error: 0.2733 - val_loss: 0.1149 - val_mean_absolute_error: 0.2542 Epoch 00123: val_loss did not improve from 0.10685 Epoch 124/5000 102/102 [==============================] - ETA: 0s - loss: 0.1129 - mean_absolute_error: 0.278 - 0s 215us/step - loss: 0.1304 - mean_absolute_error: 0.2919 - val_loss: 0.1174 - val_mean_absolute_error: 0.2561 Epoch 00124: val_loss did not improve from 0.10685 Epoch 125/5000 102/102 [==============================] - ETA: 0s - loss: 0.1727 - mean_absolute_error: 0.331 - 0s 235us/step - loss: 0.1542 - mean_absolute_error: 0.3095 - val_loss: 0.1160 - val_mean_absolute_error: 0.2540 Epoch 00125: val_loss did not improve from 0.10685 Epoch 126/5000 102/102 [==============================] - ETA: 0s - loss: 0.1432 - mean_absolute_error: 0.306 - 0s 225us/step - loss: 0.1373 - mean_absolute_error: 0.2947 - val_loss: 0.1102 - val_mean_absolute_error: 0.2483 Epoch 00126: val_loss did not improve from 0.10685 Epoch 127/5000 102/102 [==============================] - ETA: 0s - loss: 0.1793 - mean_absolute_error: 0.349 - 0s 244us/step - loss: 0.1520 - mean_absolute_error: 0.3152 - val_loss: 0.1047 - val_mean_absolute_error: 0.2428 Epoch 00127: val_loss improved from 0.10685 to 0.10473, saving model to find_phone_dnn_model.h5 Epoch 128/5000 102/102 [==============================] - ETA: 0s - loss: 0.1725 - mean_absolute_error: 0.337 - 0s 196us/step - loss: 0.1626 - mean_absolute_error: 0.3230 - val_loss: 0.1040 - val_mean_absolute_error: 0.2427 Epoch 00128: val_loss improved from 0.10473 to 0.10397, saving model to find_phone_dnn_model.h5 Epoch 129/5000 102/102 [==============================] - ETA: 0s - loss: 0.1234 - mean_absolute_error: 0.279 - 0s 234us/step - loss: 0.1365 - mean_absolute_error: 0.2985 - val_loss: 0.1104 - val_mean_absolute_error: 0.2510 Epoch 00129: val_loss did not improve from 0.10397 Epoch 130/5000 102/102 [==============================] - ETA: 0s - loss: 0.1618 - mean_absolute_error: 0.324 - 0s 186us/step - loss: 0.1603 - mean_absolute_error: 0.3219 - val_loss: 0.1164 - val_mean_absolute_error: 0.2590 Epoch 00130: val_loss did not improve from 0.10397 Epoch 131/5000 102/102 [==============================] - ETA: 0s - loss: 0.1544 - mean_absolute_error: 0.324 - 0s 215us/step - loss: 0.1313 - mean_absolute_error: 0.2926 - val_loss: 0.1197 - val_mean_absolute_error: 0.2630 Epoch 00131: val_loss did not improve from 0.10397 Epoch 132/5000 102/102 [==============================] - ETA: 0s - loss: 0.2112 - mean_absolute_error: 0.359 - 0s 196us/step - loss: 0.1610 - mean_absolute_error: 0.3205 - val_loss: 0.1189 - val_mean_absolute_error: 0.2622 Epoch 00132: val_loss did not improve from 0.10397 Epoch 133/5000 102/102 [==============================] - ETA: 0s - loss: 0.1679 - mean_absolute_error: 0.341 - 0s 215us/step - loss: 0.1517 - mean_absolute_error: 0.3132 - val_loss: 0.1178 - val_mean_absolute_error: 0.2609 Epoch 00133: val_loss did not improve from 0.10397 Epoch 134/5000 102/102 [==============================] - ETA: 0s - loss: 0.1622 - mean_absolute_error: 0.321 - 0s 215us/step - loss: 0.1476 - mean_absolute_error: 0.3034 - val_loss: 0.1163 - val_mean_absolute_error: 0.2600 Epoch 00134: val_loss did not improve from 0.10397 Epoch 135/5000 102/102 [==============================] - ETA: 0s - loss: 0.1231 - mean_absolute_error: 0.284 - 0s 186us/step - loss: 0.1353 - mean_absolute_error: 0.3012 - val_loss: 0.1163 - val_mean_absolute_error: 0.2606 Epoch 00135: val_loss did not improve from 0.10397 Epoch 136/5000 102/102 [==============================] - ETA: 0s - loss: 0.1708 - mean_absolute_error: 0.342 - 0s 186us/step - loss: 0.1567 - mean_absolute_error: 0.3205 - val_loss: 0.1156 - val_mean_absolute_error: 0.2600 Epoch 00136: val_loss did not improve from 0.10397 Epoch 137/5000 102/102 [==============================] - ETA: 0s - loss: 0.1409 - mean_absolute_error: 0.307 - 0s 186us/step - loss: 0.1426 - mean_absolute_error: 0.3060 - val_loss: 0.1143 - val_mean_absolute_error: 0.2585 Epoch 00137: val_loss did not improve from 0.10397 Epoch 138/5000 102/102 [==============================] - ETA: 0s - loss: 0.1492 - mean_absolute_error: 0.306 - 0s 205us/step - loss: 0.1486 - mean_absolute_error: 0.3108 - val_loss: 0.1129 - val_mean_absolute_error: 0.2570 Epoch 00138: val_loss did not improve from 0.10397 Epoch 139/5000 102/102 [==============================] - ETA: 0s - loss: 0.1217 - mean_absolute_error: 0.274 - 0s 205us/step - loss: 0.1413 - mean_absolute_error: 0.3039 - val_loss: 0.1118 - val_mean_absolute_error: 0.2563 Epoch 00139: val_loss did not improve from 0.10397 Epoch 140/5000 102/102 [==============================] - ETA: 0s - loss: 0.1397 - mean_absolute_error: 0.305 - 0s 196us/step - loss: 0.1435 - mean_absolute_error: 0.3047 - val_loss: 0.1115 - val_mean_absolute_error: 0.2559 Epoch 00140: val_loss did not improve from 0.10397 Epoch 141/5000 102/102 [==============================] - ETA: 0s - loss: 0.1018 - mean_absolute_error: 0.257 - 0s 205us/step - loss: 0.1301 - mean_absolute_error: 0.2906 - val_loss: 0.1103 - val_mean_absolute_error: 0.2544 Epoch 00141: val_loss did not improve from 0.10397 Epoch 142/5000 102/102 [==============================] - ETA: 0s - loss: 0.1505 - mean_absolute_error: 0.306 - 0s 215us/step - loss: 0.1294 - mean_absolute_error: 0.2857 - val_loss: 0.1084 - val_mean_absolute_error: 0.2515 Epoch 00142: val_loss did not improve from 0.10397 Epoch 143/5000 102/102 [==============================] - ETA: 0s - loss: 0.1088 - mean_absolute_error: 0.271 - 0s 205us/step - loss: 0.1339 - mean_absolute_error: 0.2923 - val_loss: 0.1085 - val_mean_absolute_error: 0.2503 Epoch 00143: val_loss did not improve from 0.10397 Epoch 144/5000 102/102 [==============================] - ETA: 0s - loss: 0.1431 - mean_absolute_error: 0.305 - 0s 196us/step - loss: 0.1310 - mean_absolute_error: 0.2899 - val_loss: 0.1066 - val_mean_absolute_error: 0.2470 Epoch 00144: val_loss did not improve from 0.10397 Epoch 145/5000 102/102 [==============================] - ETA: 0s - loss: 0.1197 - mean_absolute_error: 0.281 - 0s 215us/step - loss: 0.1320 - mean_absolute_error: 0.2913 - val_loss: 0.1051 - val_mean_absolute_error: 0.2446 Epoch 00145: val_loss did not improve from 0.10397 Epoch 146/5000 102/102 [==============================] - ETA: 0s - loss: 0.1316 - mean_absolute_error: 0.294 - 0s 303us/step - loss: 0.1463 - mean_absolute_error: 0.3064 - val_loss: 0.1036 - val_mean_absolute_error: 0.2429 Epoch 00146: val_loss improved from 0.10397 to 0.10363, saving model to find_phone_dnn_model.h5 Epoch 147/5000 102/102 [==============================] - ETA: 0s - loss: 0.1225 - mean_absolute_error: 0.272 - 0s 225us/step - loss: 0.1258 - mean_absolute_error: 0.2770 - val_loss: 0.1026 - val_mean_absolute_error: 0.2417 Epoch 00147: val_loss improved from 0.10363 to 0.10261, saving model to find_phone_dnn_model.h5 Epoch 148/5000 102/102 [==============================] - ETA: 0s - loss: 0.1538 - mean_absolute_error: 0.319 - 0s 196us/step - loss: 0.1262 - mean_absolute_error: 0.2848 - val_loss: 0.1016 - val_mean_absolute_error: 0.2408 Epoch 00148: val_loss improved from 0.10261 to 0.10159, saving model to find_phone_dnn_model.h5 Epoch 149/5000 102/102 [==============================] - ETA: 0s - loss: 0.1610 - mean_absolute_error: 0.327 - 0s 186us/step - loss: 0.1299 - mean_absolute_error: 0.2828 - val_loss: 0.1008 - val_mean_absolute_error: 0.2411 Epoch 00149: val_loss improved from 0.10159 to 0.10080, saving model to find_phone_dnn_model.h5 Epoch 150/5000 102/102 [==============================] - ETA: 0s - loss: 0.1359 - mean_absolute_error: 0.291 - 0s 215us/step - loss: 0.1414 - mean_absolute_error: 0.3012 - val_loss: 0.0984 - val_mean_absolute_error: 0.2393 Epoch 00150: val_loss improved from 0.10080 to 0.09840, saving model to find_phone_dnn_model.h5 Epoch 151/5000 102/102 [==============================] - ETA: 0s - loss: 0.1428 - mean_absolute_error: 0.296 - 0s 235us/step - loss: 0.1484 - mean_absolute_error: 0.3129 - val_loss: 0.0950 - val_mean_absolute_error: 0.2358 Epoch 00151: val_loss improved from 0.09840 to 0.09497, saving model to find_phone_dnn_model.h5 Epoch 152/5000 102/102 [==============================] - ETA: 0s - loss: 0.1060 - mean_absolute_error: 0.257 - 0s 176us/step - loss: 0.1200 - mean_absolute_error: 0.2826 - val_loss: 0.0929 - val_mean_absolute_error: 0.2330 Epoch 00152: val_loss improved from 0.09497 to 0.09295, saving model to find_phone_dnn_model.h5 Epoch 153/5000 102/102 [==============================] - ETA: 0s - loss: 0.0768 - mean_absolute_error: 0.221 - 0s 176us/step - loss: 0.1102 - mean_absolute_error: 0.2617 - val_loss: 0.0956 - val_mean_absolute_error: 0.2358 Epoch 00153: val_loss did not improve from 0.09295 Epoch 154/5000 102/102 [==============================] - ETA: 0s - loss: 0.1129 - mean_absolute_error: 0.260 - 0s 186us/step - loss: 0.1189 - mean_absolute_error: 0.2737 - val_loss: 0.1006 - val_mean_absolute_error: 0.2413 Epoch 00154: val_loss did not improve from 0.09295 Epoch 155/5000 102/102 [==============================] - ETA: 0s - loss: 0.1300 - mean_absolute_error: 0.290 - 0s 186us/step - loss: 0.1346 - mean_absolute_error: 0.2918 - val_loss: 0.1070 - val_mean_absolute_error: 0.2479 Epoch 00155: val_loss did not improve from 0.09295 Epoch 156/5000 102/102 [==============================] - ETA: 0s - loss: 0.1462 - mean_absolute_error: 0.311 - 0s 205us/step - loss: 0.1322 - mean_absolute_error: 0.2927 - val_loss: 0.1086 - val_mean_absolute_error: 0.2487 Epoch 00156: val_loss did not improve from 0.09295 Epoch 157/5000 102/102 [==============================] - ETA: 0s - loss: 0.1175 - mean_absolute_error: 0.270 - 0s 205us/step - loss: 0.1185 - mean_absolute_error: 0.2715 - val_loss: 0.1089 - val_mean_absolute_error: 0.2485 Epoch 00157: val_loss did not improve from 0.09295 Epoch 158/5000 102/102 [==============================] - ETA: 0s - loss: 0.1614 - mean_absolute_error: 0.322 - 0s 196us/step - loss: 0.1501 - mean_absolute_error: 0.3103 - val_loss: 0.1088 - val_mean_absolute_error: 0.2475 Epoch 00158: val_loss did not improve from 0.09295 Epoch 159/5000 102/102 [==============================] - ETA: 0s - loss: 0.1499 - mean_absolute_error: 0.317 - 0s 205us/step - loss: 0.1366 - mean_absolute_error: 0.2957 - val_loss: 0.1065 - val_mean_absolute_error: 0.2442 Epoch 00159: val_loss did not improve from 0.09295 Epoch 160/5000 102/102 [==============================] - ETA: 0s - loss: 0.1213 - mean_absolute_error: 0.284 - 0s 215us/step - loss: 0.1142 - mean_absolute_error: 0.2662 - val_loss: 0.1023 - val_mean_absolute_error: 0.2389 Epoch 00160: val_loss did not improve from 0.09295 Epoch 161/5000 102/102 [==============================] - ETA: 0s - loss: 0.1340 - mean_absolute_error: 0.314 - 0s 205us/step - loss: 0.1167 - mean_absolute_error: 0.2719 - val_loss: 0.1008 - val_mean_absolute_error: 0.2375 Epoch 00161: val_loss did not improve from 0.09295 Epoch 162/5000 102/102 [==============================] - ETA: 0s - loss: 0.0955 - mean_absolute_error: 0.237 - 0s 196us/step - loss: 0.1000 - mean_absolute_error: 0.2443 - val_loss: 0.1011 - val_mean_absolute_error: 0.2379 Epoch 00162: val_loss did not improve from 0.09295 Epoch 163/5000 102/102 [==============================] - ETA: 0s - loss: 0.1432 - mean_absolute_error: 0.299 - 0s 215us/step - loss: 0.1302 - mean_absolute_error: 0.2953 - val_loss: 0.1023 - val_mean_absolute_error: 0.2397 Epoch 00163: val_loss did not improve from 0.09295 Epoch 164/5000 102/102 [==============================] - ETA: 0s - loss: 0.1058 - mean_absolute_error: 0.263 - 0s 196us/step - loss: 0.1269 - mean_absolute_error: 0.2853 - val_loss: 0.1037 - val_mean_absolute_error: 0.2417 Epoch 00164: val_loss did not improve from 0.09295 Epoch 165/5000 102/102 [==============================] - ETA: 0s - loss: 0.1474 - mean_absolute_error: 0.304 - 0s 205us/step - loss: 0.1306 - mean_absolute_error: 0.2887 - val_loss: 0.1050 - val_mean_absolute_error: 0.2439 Epoch 00165: val_loss did not improve from 0.09295 Epoch 166/5000 102/102 [==============================] - ETA: 0s - loss: 0.1324 - mean_absolute_error: 0.302 - 0s 196us/step - loss: 0.1198 - mean_absolute_error: 0.2729 - val_loss: 0.1050 - val_mean_absolute_error: 0.2446 Epoch 00166: val_loss did not improve from 0.09295 Epoch 167/5000 102/102 [==============================] - ETA: 0s - loss: 0.0856 - mean_absolute_error: 0.225 - 0s 205us/step - loss: 0.1072 - mean_absolute_error: 0.2541 - val_loss: 0.1030 - val_mean_absolute_error: 0.2428 Epoch 00167: val_loss did not improve from 0.09295 Epoch 168/5000 102/102 [==============================] - ETA: 0s - loss: 0.1825 - mean_absolute_error: 0.353 - 0s 196us/step - loss: 0.1257 - mean_absolute_error: 0.2832 - val_loss: 0.1042 - val_mean_absolute_error: 0.2439 Epoch 00168: val_loss did not improve from 0.09295 Epoch 169/5000 102/102 [==============================] - ETA: 0s - loss: 0.0956 - mean_absolute_error: 0.243 - 0s 205us/step - loss: 0.1130 - mean_absolute_error: 0.2632 - val_loss: 0.1050 - val_mean_absolute_error: 0.2440 Epoch 00169: val_loss did not improve from 0.09295 Epoch 170/5000 102/102 [==============================] - ETA: 0s - loss: 0.1744 - mean_absolute_error: 0.340 - 0s 205us/step - loss: 0.1319 - mean_absolute_error: 0.2920 - val_loss: 0.1066 - val_mean_absolute_error: 0.2453 Epoch 00170: val_loss did not improve from 0.09295 Epoch 171/5000 102/102 [==============================] - ETA: 0s - loss: 0.1146 - mean_absolute_error: 0.269 - 0s 254us/step - loss: 0.1231 - mean_absolute_error: 0.2770 - val_loss: 0.1073 - val_mean_absolute_error: 0.2461 Epoch 00171: val_loss did not improve from 0.09295 Epoch 172/5000 102/102 [==============================] - ETA: 0s - loss: 0.1423 - mean_absolute_error: 0.301 - 0s 225us/step - loss: 0.1476 - mean_absolute_error: 0.3101 - val_loss: 0.1075 - val_mean_absolute_error: 0.2467 Epoch 00172: val_loss did not improve from 0.09295 Epoch 173/5000 102/102 [==============================] - ETA: 0s - loss: 0.1570 - mean_absolute_error: 0.304 - 0s 186us/step - loss: 0.1374 - mean_absolute_error: 0.2960 - val_loss: 0.1071 - val_mean_absolute_error: 0.2470 Epoch 00173: val_loss did not improve from 0.09295 Epoch 174/5000 102/102 [==============================] - ETA: 0s - loss: 0.1520 - mean_absolute_error: 0.320 - 0s 205us/step - loss: 0.1431 - mean_absolute_error: 0.3023 - val_loss: 0.1064 - val_mean_absolute_error: 0.2468 Epoch 00174: val_loss did not improve from 0.09295 Epoch 175/5000 102/102 [==============================] - ETA: 0s - loss: 0.1224 - mean_absolute_error: 0.286 - 0s 205us/step - loss: 0.1375 - mean_absolute_error: 0.2999 - val_loss: 0.1049 - val_mean_absolute_error: 0.2448 Epoch 00175: val_loss did not improve from 0.09295 Epoch 176/5000 102/102 [==============================] - ETA: 0s - loss: 0.1303 - mean_absolute_error: 0.281 - 0s 225us/step - loss: 0.1324 - mean_absolute_error: 0.2990 - val_loss: 0.1034 - val_mean_absolute_error: 0.2425 Epoch 00176: val_loss did not improve from 0.09295 Epoch 177/5000 102/102 [==============================] - ETA: 0s - loss: 0.0973 - mean_absolute_error: 0.237 - 0s 196us/step - loss: 0.1136 - mean_absolute_error: 0.2649 - val_loss: 0.1017 - val_mean_absolute_error: 0.2397 Epoch 00177: val_loss did not improve from 0.09295 Epoch 178/5000 102/102 [==============================] - ETA: 0s - loss: 0.1210 - mean_absolute_error: 0.287 - 0s 215us/step - loss: 0.1185 - mean_absolute_error: 0.2709 - val_loss: 0.1003 - val_mean_absolute_error: 0.2376 Epoch 00178: val_loss did not improve from 0.09295 Epoch 179/5000 102/102 [==============================] - ETA: 0s - loss: 0.1149 - mean_absolute_error: 0.263 - 0s 215us/step - loss: 0.1216 - mean_absolute_error: 0.2761 - val_loss: 0.1002 - val_mean_absolute_error: 0.2370 Epoch 00179: val_loss did not improve from 0.09295 Epoch 180/5000 102/102 [==============================] - ETA: 0s - loss: 0.1120 - mean_absolute_error: 0.260 - 0s 186us/step - loss: 0.1073 - mean_absolute_error: 0.2582 - val_loss: 0.1035 - val_mean_absolute_error: 0.2405 Epoch 00180: val_loss did not improve from 0.09295 Epoch 181/5000 102/102 [==============================] - ETA: 0s - loss: 0.0831 - mean_absolute_error: 0.240 - 0s 254us/step - loss: 0.1274 - mean_absolute_error: 0.2905 - val_loss: 0.1054 - val_mean_absolute_error: 0.2426 Epoch 00181: val_loss did not improve from 0.09295 Epoch 182/5000 102/102 [==============================] - ETA: 0s - loss: 0.1049 - mean_absolute_error: 0.246 - 0s 186us/step - loss: 0.1151 - mean_absolute_error: 0.2609 - val_loss: 0.1063 - val_mean_absolute_error: 0.2440 Epoch 00182: val_loss did not improve from 0.09295 Epoch 183/5000 102/102 [==============================] - ETA: 0s - loss: 0.1175 - mean_absolute_error: 0.274 - 0s 215us/step - loss: 0.1174 - mean_absolute_error: 0.2753 - val_loss: 0.1072 - val_mean_absolute_error: 0.2445 Epoch 00183: val_loss did not improve from 0.09295 Epoch 184/5000 102/102 [==============================] - ETA: 0s - loss: 0.1092 - mean_absolute_error: 0.263 - 0s 215us/step - loss: 0.1296 - mean_absolute_error: 0.2864 - val_loss: 0.1071 - val_mean_absolute_error: 0.2437 Epoch 00184: val_loss did not improve from 0.09295 Epoch 185/5000 102/102 [==============================] - ETA: 0s - loss: 0.1429 - mean_absolute_error: 0.301 - 0s 195us/step - loss: 0.1240 - mean_absolute_error: 0.2760 - val_loss: 0.1059 - val_mean_absolute_error: 0.2425 Epoch 00185: val_loss did not improve from 0.09295 Epoch 186/5000 102/102 [==============================] - ETA: 0s - loss: 0.1461 - mean_absolute_error: 0.303 - 0s 225us/step - loss: 0.1354 - mean_absolute_error: 0.2941 - val_loss: 0.1054 - val_mean_absolute_error: 0.2424 Epoch 00186: val_loss did not improve from 0.09295 Epoch 187/5000 102/102 [==============================] - ETA: 0s - loss: 0.1372 - mean_absolute_error: 0.294 - 0s 205us/step - loss: 0.1336 - mean_absolute_error: 0.2912 - val_loss: 0.1064 - val_mean_absolute_error: 0.2444 Epoch 00187: val_loss did not improve from 0.09295 Epoch 188/5000 102/102 [==============================] - ETA: 0s - loss: 0.1123 - mean_absolute_error: 0.266 - 0s 205us/step - loss: 0.1236 - mean_absolute_error: 0.2826 - val_loss: 0.1060 - val_mean_absolute_error: 0.2447 Epoch 00188: val_loss did not improve from 0.09295 Epoch 189/5000 102/102 [==============================] - ETA: 0s - loss: 0.1185 - mean_absolute_error: 0.272 - 0s 196us/step - loss: 0.1157 - mean_absolute_error: 0.2664 - val_loss: 0.1062 - val_mean_absolute_error: 0.2456 Epoch 00189: val_loss did not improve from 0.09295 Epoch 190/5000 102/102 [==============================] - ETA: 0s - loss: 0.0882 - mean_absolute_error: 0.236 - 0s 215us/step - loss: 0.1213 - mean_absolute_error: 0.2728 - val_loss: 0.1074 - val_mean_absolute_error: 0.2474 Epoch 00190: val_loss did not improve from 0.09295 Epoch 191/5000 102/102 [==============================] - ETA: 0s - loss: 0.1502 - mean_absolute_error: 0.316 - 0s 205us/step - loss: 0.1197 - mean_absolute_error: 0.2738 - val_loss: 0.1070 - val_mean_absolute_error: 0.2467 Epoch 00191: val_loss did not improve from 0.09295 Epoch 192/5000 102/102 [==============================] - ETA: 0s - loss: 0.0856 - mean_absolute_error: 0.222 - 0s 196us/step - loss: 0.1070 - mean_absolute_error: 0.2550 - val_loss: 0.1033 - val_mean_absolute_error: 0.2421 Epoch 00192: val_loss did not improve from 0.09295 Epoch 193/5000 102/102 [==============================] - ETA: 0s - loss: 0.1067 - mean_absolute_error: 0.256 - 0s 205us/step - loss: 0.1137 - mean_absolute_error: 0.2720 - val_loss: 0.1006 - val_mean_absolute_error: 0.2386 Epoch 00193: val_loss did not improve from 0.09295 Epoch 194/5000 102/102 [==============================] - ETA: 0s - loss: 0.1075 - mean_absolute_error: 0.259 - 0s 196us/step - loss: 0.1240 - mean_absolute_error: 0.2850 - val_loss: 0.0996 - val_mean_absolute_error: 0.2381 Epoch 00194: val_loss did not improve from 0.09295 Epoch 195/5000 102/102 [==============================] - ETA: 0s - loss: 0.1122 - mean_absolute_error: 0.261 - 0s 166us/step - loss: 0.1142 - mean_absolute_error: 0.2638 - val_loss: 0.0992 - val_mean_absolute_error: 0.2382 Epoch 00195: val_loss did not improve from 0.09295 Epoch 196/5000 102/102 [==============================] - ETA: 0s - loss: 0.0752 - mean_absolute_error: 0.222 - 0s 176us/step - loss: 0.0945 - mean_absolute_error: 0.2468 - val_loss: 0.0991 - val_mean_absolute_error: 0.2380 Epoch 00196: val_loss did not improve from 0.09295 Epoch 197/5000 102/102 [==============================] - ETA: 0s - loss: 0.0939 - mean_absolute_error: 0.246 - 0s 205us/step - loss: 0.1153 - mean_absolute_error: 0.2699 - val_loss: 0.0995 - val_mean_absolute_error: 0.2382 Epoch 00197: val_loss did not improve from 0.09295 Epoch 198/5000 102/102 [==============================] - ETA: 0s - loss: 0.0861 - mean_absolute_error: 0.218 - 0s 205us/step - loss: 0.1096 - mean_absolute_error: 0.2592 - val_loss: 0.1005 - val_mean_absolute_error: 0.2386 Epoch 00198: val_loss did not improve from 0.09295 Epoch 199/5000 102/102 [==============================] - ETA: 0s - loss: 0.1190 - mean_absolute_error: 0.283 - 0s 205us/step - loss: 0.1044 - mean_absolute_error: 0.2547 - val_loss: 0.1015 - val_mean_absolute_error: 0.2389 Epoch 00199: val_loss did not improve from 0.09295 Epoch 200/5000 102/102 [==============================] - ETA: 0s - loss: 0.1160 - mean_absolute_error: 0.263 - 0s 186us/step - loss: 0.1042 - mean_absolute_error: 0.2480 - val_loss: 0.1015 - val_mean_absolute_error: 0.2385 Epoch 00200: val_loss did not improve from 0.09295 Epoch 201/5000 102/102 [==============================] - ETA: 0s - loss: 0.0824 - mean_absolute_error: 0.226 - 0s 244us/step - loss: 0.0957 - mean_absolute_error: 0.2373 - val_loss: 0.1018 - val_mean_absolute_error: 0.2383 Epoch 00201: val_loss did not improve from 0.09295 Epoch 202/5000 102/102 [==============================] - ETA: 0s - loss: 0.1137 - mean_absolute_error: 0.254 - 0s 215us/step - loss: 0.1135 - mean_absolute_error: 0.2636 - val_loss: 0.1012 - val_mean_absolute_error: 0.2369 Epoch 00202: val_loss did not improve from 0.09295 Epoch 203/5000 102/102 [==============================] - ETA: 0s - loss: 0.0972 - mean_absolute_error: 0.247 - 0s 215us/step - loss: 0.1017 - mean_absolute_error: 0.2483 - val_loss: 0.1007 - val_mean_absolute_error: 0.2360 Epoch 00203: val_loss did not improve from 0.09295 Epoch 204/5000 102/102 [==============================] - ETA: 0s - loss: 0.0981 - mean_absolute_error: 0.243 - 0s 196us/step - loss: 0.1066 - mean_absolute_error: 0.2534 - val_loss: 0.1010 - val_mean_absolute_error: 0.2356 Epoch 00204: val_loss did not improve from 0.09295 Epoch 205/5000 102/102 [==============================] - ETA: 0s - loss: 0.1517 - mean_absolute_error: 0.306 - 0s 215us/step - loss: 0.1263 - mean_absolute_error: 0.2819 - val_loss: 0.1011 - val_mean_absolute_error: 0.2356 Epoch 00205: val_loss did not improve from 0.09295 Epoch 206/5000 102/102 [==============================] - ETA: 0s - loss: 0.1205 - mean_absolute_error: 0.271 - 0s 215us/step - loss: 0.1045 - mean_absolute_error: 0.2512 - val_loss: 0.1027 - val_mean_absolute_error: 0.2373 Epoch 00206: val_loss did not improve from 0.09295 Epoch 207/5000 102/102 [==============================] - ETA: 0s - loss: 0.1204 - mean_absolute_error: 0.267 - 0s 196us/step - loss: 0.1254 - mean_absolute_error: 0.2786 - val_loss: 0.1043 - val_mean_absolute_error: 0.2394 Epoch 00207: val_loss did not improve from 0.09295 Epoch 208/5000 102/102 [==============================] - ETA: 0s - loss: 0.0919 - mean_absolute_error: 0.236 - 0s 186us/step - loss: 0.1165 - mean_absolute_error: 0.2636 - val_loss: 0.1053 - val_mean_absolute_error: 0.2410 Epoch 00208: val_loss did not improve from 0.09295 Epoch 209/5000 102/102 [==============================] - ETA: 0s - loss: 0.0952 - mean_absolute_error: 0.237 - 0s 205us/step - loss: 0.1058 - mean_absolute_error: 0.2506 - val_loss: 0.1040 - val_mean_absolute_error: 0.2402 Epoch 00209: val_loss did not improve from 0.09295 Epoch 210/5000 102/102 [==============================] - ETA: 0s - loss: 0.1282 - mean_absolute_error: 0.283 - 0s 196us/step - loss: 0.1247 - mean_absolute_error: 0.2797 - val_loss: 0.1001 - val_mean_absolute_error: 0.2371 Epoch 00210: val_loss did not improve from 0.09295 Epoch 211/5000 102/102 [==============================] - ETA: 0s - loss: 0.1256 - mean_absolute_error: 0.274 - 0s 196us/step - loss: 0.1148 - mean_absolute_error: 0.2669 - val_loss: 0.0939 - val_mean_absolute_error: 0.2313 Epoch 00211: val_loss did not improve from 0.09295 Epoch 212/5000 102/102 [==============================] - ETA: 0s - loss: 0.1272 - mean_absolute_error: 0.292 - 0s 205us/step - loss: 0.1113 - mean_absolute_error: 0.2663 - val_loss: 0.0915 - val_mean_absolute_error: 0.2288 Epoch 00212: val_loss improved from 0.09295 to 0.09154, saving model to find_phone_dnn_model.h5 Epoch 213/5000 102/102 [==============================] - ETA: 0s - loss: 0.1251 - mean_absolute_error: 0.252 - 0s 176us/step - loss: 0.1176 - mean_absolute_error: 0.2630 - val_loss: 0.0924 - val_mean_absolute_error: 0.2301 Epoch 00213: val_loss did not improve from 0.09154 Epoch 214/5000 102/102 [==============================] - ETA: 0s - loss: 0.1131 - mean_absolute_error: 0.258 - 0s 554us/step - loss: 0.0979 - mean_absolute_error: 0.2412 - val_loss: 0.0942 - val_mean_absolute_error: 0.2314 Epoch 00214: val_loss did not improve from 0.09154 Epoch 215/5000 102/102 [==============================] - ETA: 0s - loss: 0.0970 - mean_absolute_error: 0.243 - 0s 293us/step - loss: 0.0977 - mean_absolute_error: 0.2436 - val_loss: 0.0977 - val_mean_absolute_error: 0.2349 Epoch 00215: val_loss did not improve from 0.09154 Epoch 216/5000 102/102 [==============================] - ETA: 0s - loss: 0.1230 - mean_absolute_error: 0.289 - 0s 205us/step - loss: 0.1291 - mean_absolute_error: 0.2884 - val_loss: 0.1006 - val_mean_absolute_error: 0.2371 Epoch 00216: val_loss did not improve from 0.09154 Epoch 217/5000 102/102 [==============================] - ETA: 0s - loss: 0.1282 - mean_absolute_error: 0.294 - 0s 205us/step - loss: 0.1082 - mean_absolute_error: 0.2622 - val_loss: 0.0996 - val_mean_absolute_error: 0.2366 Epoch 00217: val_loss did not improve from 0.09154 Epoch 218/5000 102/102 [==============================] - ETA: 0s - loss: 0.0997 - mean_absolute_error: 0.249 - 0s 235us/step - loss: 0.1111 - mean_absolute_error: 0.2615 - val_loss: 0.0990 - val_mean_absolute_error: 0.2363 Epoch 00218: val_loss did not improve from 0.09154 Epoch 219/5000 102/102 [==============================] - ETA: 0s - loss: 0.0956 - mean_absolute_error: 0.241 - 0s 205us/step - loss: 0.1137 - mean_absolute_error: 0.2670 - val_loss: 0.0981 - val_mean_absolute_error: 0.2351 Epoch 00219: val_loss did not improve from 0.09154 Epoch 220/5000 102/102 [==============================] - ETA: 0s - loss: 0.1390 - mean_absolute_error: 0.290 - 0s 205us/step - loss: 0.1097 - mean_absolute_error: 0.2562 - val_loss: 0.0970 - val_mean_absolute_error: 0.2335 Epoch 00220: val_loss did not improve from 0.09154 Epoch 221/5000 102/102 [==============================] - ETA: 0s - loss: 0.0685 - mean_absolute_error: 0.211 - 0s 196us/step - loss: 0.0970 - mean_absolute_error: 0.2468 - val_loss: 0.0974 - val_mean_absolute_error: 0.2334 Epoch 00221: val_loss did not improve from 0.09154 Epoch 222/5000 102/102 [==============================] - ETA: 0s - loss: 0.1150 - mean_absolute_error: 0.250 - 0s 205us/step - loss: 0.1124 - mean_absolute_error: 0.2589 - val_loss: 0.1006 - val_mean_absolute_error: 0.2352 Epoch 00222: val_loss did not improve from 0.09154 Epoch 223/5000 102/102 [==============================] - ETA: 0s - loss: 0.0783 - mean_absolute_error: 0.209 - 0s 244us/step - loss: 0.1034 - mean_absolute_error: 0.2459 - val_loss: 0.1037 - val_mean_absolute_error: 0.2373 Epoch 00223: val_loss did not improve from 0.09154 Epoch 224/5000 102/102 [==============================] - ETA: 0s - loss: 0.0973 - mean_absolute_error: 0.246 - 0s 215us/step - loss: 0.1095 - mean_absolute_error: 0.2630 - val_loss: 0.1034 - val_mean_absolute_error: 0.2371 Epoch 00224: val_loss did not improve from 0.09154 Epoch 225/5000 102/102 [==============================] - ETA: 0s - loss: 0.1824 - mean_absolute_error: 0.338 - 0s 196us/step - loss: 0.1549 - mean_absolute_error: 0.3087 - val_loss: 0.1027 - val_mean_absolute_error: 0.2369 Epoch 00225: val_loss did not improve from 0.09154 Epoch 226/5000 102/102 [==============================] - ETA: 0s - loss: 0.1072 - mean_absolute_error: 0.271 - 0s 244us/step - loss: 0.1121 - mean_absolute_error: 0.2632 - val_loss: 0.1015 - val_mean_absolute_error: 0.2364 Epoch 00226: val_loss did not improve from 0.09154 Epoch 227/5000 102/102 [==============================] - ETA: 0s - loss: 0.1330 - mean_absolute_error: 0.296 - 0s 235us/step - loss: 0.1165 - mean_absolute_error: 0.2712 - val_loss: 0.0999 - val_mean_absolute_error: 0.2351 Epoch 00227: val_loss did not improve from 0.09154 Epoch 228/5000 102/102 [==============================] - ETA: 0s - loss: 0.1424 - mean_absolute_error: 0.312 - 0s 215us/step - loss: 0.1172 - mean_absolute_error: 0.2756 - val_loss: 0.0981 - val_mean_absolute_error: 0.2336 Epoch 00228: val_loss did not improve from 0.09154 Epoch 229/5000 102/102 [==============================] - ETA: 0s - loss: 0.1085 - mean_absolute_error: 0.259 - 0s 225us/step - loss: 0.1080 - mean_absolute_error: 0.2551 - val_loss: 0.0973 - val_mean_absolute_error: 0.2325 Epoch 00229: val_loss did not improve from 0.09154 Epoch 230/5000 102/102 [==============================] - ETA: 0s - loss: 0.0944 - mean_absolute_error: 0.254 - 0s 205us/step - loss: 0.1145 - mean_absolute_error: 0.2705 - val_loss: 0.0968 - val_mean_absolute_error: 0.2315 Epoch 00230: val_loss did not improve from 0.09154 Epoch 231/5000 102/102 [==============================] - ETA: 0s - loss: 0.1207 - mean_absolute_error: 0.264 - 0s 215us/step - loss: 0.1225 - mean_absolute_error: 0.2742 - val_loss: 0.0953 - val_mean_absolute_error: 0.2291 Epoch 00231: val_loss did not improve from 0.09154 Epoch 232/5000 102/102 [==============================] - ETA: 0s - loss: 0.1352 - mean_absolute_error: 0.288 - 0s 235us/step - loss: 0.1061 - mean_absolute_error: 0.2499 - val_loss: 0.0947 - val_mean_absolute_error: 0.2280 Epoch 00232: val_loss did not improve from 0.09154 Epoch 233/5000 102/102 [==============================] - ETA: 0s - loss: 0.1083 - mean_absolute_error: 0.254 - 0s 254us/step - loss: 0.1082 - mean_absolute_error: 0.2610 - val_loss: 0.0950 - val_mean_absolute_error: 0.2278 Epoch 00233: val_loss did not improve from 0.09154 Epoch 234/5000 102/102 [==============================] - ETA: 0s - loss: 0.1179 - mean_absolute_error: 0.278 - 0s 215us/step - loss: 0.1120 - mean_absolute_error: 0.2608 - val_loss: 0.0943 - val_mean_absolute_error: 0.2269 Epoch 00234: val_loss did not improve from 0.09154 Epoch 235/5000 102/102 [==============================] - ETA: 0s - loss: 0.1134 - mean_absolute_error: 0.275 - 0s 186us/step - loss: 0.0872 - mean_absolute_error: 0.2388 - val_loss: 0.0941 - val_mean_absolute_error: 0.2267 Epoch 00235: val_loss did not improve from 0.09154 Epoch 236/5000 102/102 [==============================] - ETA: 0s - loss: 0.1386 - mean_absolute_error: 0.301 - 0s 205us/step - loss: 0.1154 - mean_absolute_error: 0.2680 - val_loss: 0.0949 - val_mean_absolute_error: 0.2279 Epoch 00236: val_loss did not improve from 0.09154 Epoch 237/5000 102/102 [==============================] - ETA: 0s - loss: 0.1051 - mean_absolute_error: 0.258 - 0s 186us/step - loss: 0.1062 - mean_absolute_error: 0.2581 - val_loss: 0.0944 - val_mean_absolute_error: 0.2288 Epoch 00237: val_loss did not improve from 0.09154 Epoch 238/5000 102/102 [==============================] - ETA: 0s - loss: 0.1125 - mean_absolute_error: 0.254 - 0s 244us/step - loss: 0.1054 - mean_absolute_error: 0.2542 - val_loss: 0.0958 - val_mean_absolute_error: 0.2312 Epoch 00238: val_loss did not improve from 0.09154 Epoch 239/5000 102/102 [==============================] - ETA: 0s - loss: 0.1051 - mean_absolute_error: 0.265 - 0s 244us/step - loss: 0.1077 - mean_absolute_error: 0.2575 - val_loss: 0.0985 - val_mean_absolute_error: 0.2338 Epoch 00239: val_loss did not improve from 0.09154 Epoch 240/5000 102/102 [==============================] - ETA: 0s - loss: 0.1285 - mean_absolute_error: 0.287 - 0s 215us/step - loss: 0.1149 - mean_absolute_error: 0.2630 - val_loss: 0.0969 - val_mean_absolute_error: 0.2319 Epoch 00240: val_loss did not improve from 0.09154 Epoch 241/5000 102/102 [==============================] - ETA: 0s - loss: 0.1029 - mean_absolute_error: 0.244 - 0s 215us/step - loss: 0.1179 - mean_absolute_error: 0.2754 - val_loss: 0.0953 - val_mean_absolute_error: 0.2297 Epoch 00241: val_loss did not improve from 0.09154 Epoch 242/5000 102/102 [==============================] - ETA: 0s - loss: 0.0982 - mean_absolute_error: 0.251 - 0s 225us/step - loss: 0.0958 - mean_absolute_error: 0.2408 - val_loss: 0.0925 - val_mean_absolute_error: 0.2265 Epoch 00242: val_loss did not improve from 0.09154 Epoch 243/5000 102/102 [==============================] - ETA: 0s - loss: 0.0811 - mean_absolute_error: 0.212 - 0s 254us/step - loss: 0.0995 - mean_absolute_error: 0.2418 - val_loss: 0.0904 - val_mean_absolute_error: 0.2252 Epoch 00243: val_loss improved from 0.09154 to 0.09035, saving model to find_phone_dnn_model.h5 Epoch 244/5000 102/102 [==============================] - ETA: 0s - loss: 0.0780 - mean_absolute_error: 0.206 - 0s 225us/step - loss: 0.1011 - mean_absolute_error: 0.2469 - val_loss: 0.0898 - val_mean_absolute_error: 0.2253 Epoch 00244: val_loss improved from 0.09035 to 0.08979, saving model to find_phone_dnn_model.h5 Epoch 245/5000 102/102 [==============================] - ETA: 0s - loss: 0.1146 - mean_absolute_error: 0.266 - 0s 186us/step - loss: 0.1050 - mean_absolute_error: 0.2559 - val_loss: 0.0911 - val_mean_absolute_error: 0.2270 Epoch 00245: val_loss did not improve from 0.08979 Epoch 246/5000 102/102 [==============================] - ETA: 0s - loss: 0.0868 - mean_absolute_error: 0.227 - 0s 205us/step - loss: 0.1128 - mean_absolute_error: 0.2644 - val_loss: 0.0935 - val_mean_absolute_error: 0.2302 Epoch 00246: val_loss did not improve from 0.08979 Epoch 247/5000 102/102 [==============================] - ETA: 0s - loss: 0.0966 - mean_absolute_error: 0.235 - 0s 215us/step - loss: 0.1164 - mean_absolute_error: 0.2666 - val_loss: 0.0951 - val_mean_absolute_error: 0.2324 Epoch 00247: val_loss did not improve from 0.08979 Epoch 248/5000 102/102 [==============================] - ETA: 0s - loss: 0.0691 - mean_absolute_error: 0.207 - 0s 196us/step - loss: 0.1001 - mean_absolute_error: 0.2482 - val_loss: 0.0954 - val_mean_absolute_error: 0.2338 Epoch 00248: val_loss did not improve from 0.08979 Epoch 249/5000 102/102 [==============================] - ETA: 0s - loss: 0.1152 - mean_absolute_error: 0.258 - 0s 225us/step - loss: 0.1095 - mean_absolute_error: 0.2576 - val_loss: 0.0948 - val_mean_absolute_error: 0.2343 Epoch 00249: val_loss did not improve from 0.08979 Epoch 250/5000 102/102 [==============================] - ETA: 0s - loss: 0.0888 - mean_absolute_error: 0.238 - 0s 196us/step - loss: 0.0934 - mean_absolute_error: 0.2417 - val_loss: 0.0943 - val_mean_absolute_error: 0.2333 Epoch 00250: val_loss did not improve from 0.08979 Epoch 251/5000 102/102 [==============================] - ETA: 0s - loss: 0.0979 - mean_absolute_error: 0.241 - 0s 205us/step - loss: 0.1042 - mean_absolute_error: 0.2503 - val_loss: 0.0939 - val_mean_absolute_error: 0.2318 Epoch 00251: val_loss did not improve from 0.08979 Epoch 252/5000 102/102 [==============================] - ETA: 0s - loss: 0.0885 - mean_absolute_error: 0.223 - 0s 196us/step - loss: 0.0962 - mean_absolute_error: 0.2414 - val_loss: 0.0945 - val_mean_absolute_error: 0.2321 Epoch 00252: val_loss did not improve from 0.08979 Epoch 253/5000 102/102 [==============================] - ETA: 0s - loss: 0.0909 - mean_absolute_error: 0.229 - 0s 205us/step - loss: 0.1066 - mean_absolute_error: 0.2489 - val_loss: 0.0949 - val_mean_absolute_error: 0.2320 Epoch 00253: val_loss did not improve from 0.08979 Epoch 254/5000 102/102 [==============================] - ETA: 0s - loss: 0.0859 - mean_absolute_error: 0.220 - 0s 205us/step - loss: 0.1099 - mean_absolute_error: 0.2571 - val_loss: 0.0934 - val_mean_absolute_error: 0.2305 Epoch 00254: val_loss did not improve from 0.08979 Epoch 255/5000 102/102 [==============================] - ETA: 0s - loss: 0.1230 - mean_absolute_error: 0.278 - 0s 196us/step - loss: 0.1133 - mean_absolute_error: 0.2660 - val_loss: 0.0904 - val_mean_absolute_error: 0.2276 Epoch 00255: val_loss did not improve from 0.08979 Epoch 256/5000 102/102 [==============================] - ETA: 0s - loss: 0.1006 - mean_absolute_error: 0.250 - 0s 215us/step - loss: 0.0911 - mean_absolute_error: 0.2331 - val_loss: 0.0865 - val_mean_absolute_error: 0.2231 Epoch 00256: val_loss improved from 0.08979 to 0.08648, saving model to find_phone_dnn_model.h5 Epoch 257/5000 102/102 [==============================] - ETA: 0s - loss: 0.0941 - mean_absolute_error: 0.231 - 0s 196us/step - loss: 0.1077 - mean_absolute_error: 0.2492 - val_loss: 0.0850 - val_mean_absolute_error: 0.2210 Epoch 00257: val_loss improved from 0.08648 to 0.08505, saving model to find_phone_dnn_model.h5 Epoch 258/5000 102/102 [==============================] - ETA: 0s - loss: 0.1129 - mean_absolute_error: 0.265 - 0s 284us/step - loss: 0.1090 - mean_absolute_error: 0.2583 - val_loss: 0.0849 - val_mean_absolute_error: 0.2202 Epoch 00258: val_loss improved from 0.08505 to 0.08486, saving model to find_phone_dnn_model.h5 Epoch 259/5000 102/102 [==============================] - ETA: 0s - loss: 0.0996 - mean_absolute_error: 0.234 - 0s 196us/step - loss: 0.1040 - mean_absolute_error: 0.2459 - val_loss: 0.0861 - val_mean_absolute_error: 0.2219 Epoch 00259: val_loss did not improve from 0.08486 Epoch 260/5000 102/102 [==============================] - ETA: 0s - loss: 0.1149 - mean_absolute_error: 0.265 - 0s 264us/step - loss: 0.1192 - mean_absolute_error: 0.2636 - val_loss: 0.0863 - val_mean_absolute_error: 0.2231 Epoch 00260: val_loss did not improve from 0.08486 Epoch 261/5000 102/102 [==============================] - ETA: 0s - loss: 0.1249 - mean_absolute_error: 0.278 - 0s 225us/step - loss: 0.0957 - mean_absolute_error: 0.2371 - val_loss: 0.0894 - val_mean_absolute_error: 0.2271 Epoch 00261: val_loss did not improve from 0.08486 Epoch 262/5000 102/102 [==============================] - ETA: 0s - loss: 0.0858 - mean_absolute_error: 0.220 - 0s 235us/step - loss: 0.0903 - mean_absolute_error: 0.2286 - val_loss: 0.0929 - val_mean_absolute_error: 0.2307 Epoch 00262: val_loss did not improve from 0.08486 Epoch 263/5000 102/102 [==============================] - ETA: 0s - loss: 0.0809 - mean_absolute_error: 0.237 - 0s 244us/step - loss: 0.0947 - mean_absolute_error: 0.2499 - val_loss: 0.0958 - val_mean_absolute_error: 0.2341 Epoch 00263: val_loss did not improve from 0.08486 Epoch 264/5000 102/102 [==============================] - ETA: 0s - loss: 0.1016 - mean_absolute_error: 0.244 - 0s 215us/step - loss: 0.1090 - mean_absolute_error: 0.2555 - val_loss: 0.0983 - val_mean_absolute_error: 0.2381 Epoch 00264: val_loss did not improve from 0.08486 Epoch 265/5000 102/102 [==============================] - ETA: 0s - loss: 0.0803 - mean_absolute_error: 0.219 - 0s 225us/step - loss: 0.1026 - mean_absolute_error: 0.2468 - val_loss: 0.1001 - val_mean_absolute_error: 0.2407 Epoch 00265: val_loss did not improve from 0.08486 Epoch 266/5000 102/102 [==============================] - ETA: 0s - loss: 0.1186 - mean_absolute_error: 0.262 - 0s 235us/step - loss: 0.1083 - mean_absolute_error: 0.2522 - val_loss: 0.0998 - val_mean_absolute_error: 0.2410 Epoch 00266: val_loss did not improve from 0.08486 Epoch 267/5000 102/102 [==============================] - ETA: 0s - loss: 0.1012 - mean_absolute_error: 0.237 - 0s 196us/step - loss: 0.1015 - mean_absolute_error: 0.2504 - val_loss: 0.0990 - val_mean_absolute_error: 0.2407 Epoch 00267: val_loss did not improve from 0.08486 Epoch 268/5000 102/102 [==============================] - ETA: 0s - loss: 0.1144 - mean_absolute_error: 0.259 - 0s 166us/step - loss: 0.1203 - mean_absolute_error: 0.2694 - val_loss: 0.0993 - val_mean_absolute_error: 0.2403 Epoch 00268: val_loss did not improve from 0.08486 Epoch 269/5000 102/102 [==============================] - ETA: 0s - loss: 0.1043 - mean_absolute_error: 0.244 - 0s 205us/step - loss: 0.0935 - mean_absolute_error: 0.2348 - val_loss: 0.0991 - val_mean_absolute_error: 0.2385 Epoch 00269: val_loss did not improve from 0.08486 Epoch 270/5000 102/102 [==============================] - ETA: 0s - loss: 0.0814 - mean_absolute_error: 0.212 - 0s 205us/step - loss: 0.0982 - mean_absolute_error: 0.2390 - val_loss: 0.0991 - val_mean_absolute_error: 0.2374 Epoch 00270: val_loss did not improve from 0.08486 Epoch 271/5000 102/102 [==============================] - ETA: 0s - loss: 0.0938 - mean_absolute_error: 0.237 - 0s 205us/step - loss: 0.1042 - mean_absolute_error: 0.2528 - val_loss: 0.0995 - val_mean_absolute_error: 0.2367 Epoch 00271: val_loss did not improve from 0.08486 Epoch 272/5000 102/102 [==============================] - ETA: 0s - loss: 0.1278 - mean_absolute_error: 0.273 - 0s 186us/step - loss: 0.0997 - mean_absolute_error: 0.2450 - val_loss: 0.1000 - val_mean_absolute_error: 0.2363 Epoch 00272: val_loss did not improve from 0.08486 Epoch 273/5000 102/102 [==============================] - ETA: 0s - loss: 0.1461 - mean_absolute_error: 0.300 - 0s 235us/step - loss: 0.1209 - mean_absolute_error: 0.2658 - val_loss: 0.0995 - val_mean_absolute_error: 0.2360 Epoch 00273: val_loss did not improve from 0.08486 Epoch 274/5000 102/102 [==============================] - ETA: 0s - loss: 0.0790 - mean_absolute_error: 0.210 - 0s 176us/step - loss: 0.1003 - mean_absolute_error: 0.2380 - val_loss: 0.1002 - val_mean_absolute_error: 0.2371 Epoch 00274: val_loss did not improve from 0.08486 Epoch 275/5000 102/102 [==============================] - ETA: 0s - loss: 0.0874 - mean_absolute_error: 0.229 - 0s 176us/step - loss: 0.1024 - mean_absolute_error: 0.2529 - val_loss: 0.0993 - val_mean_absolute_error: 0.2362 Epoch 00275: val_loss did not improve from 0.08486 Epoch 276/5000 102/102 [==============================] - ETA: 0s - loss: 0.1029 - mean_absolute_error: 0.241 - 0s 206us/step - loss: 0.0967 - mean_absolute_error: 0.2421 - val_loss: 0.0971 - val_mean_absolute_error: 0.2350 Epoch 00276: val_loss did not improve from 0.08486 Epoch 277/5000 102/102 [==============================] - ETA: 0s - loss: 0.1155 - mean_absolute_error: 0.271 - 0s 205us/step - loss: 0.1206 - mean_absolute_error: 0.2715 - val_loss: 0.0959 - val_mean_absolute_error: 0.2351 Epoch 00277: val_loss did not improve from 0.08486 Epoch 278/5000 102/102 [==============================] - ETA: 0s - loss: 0.1040 - mean_absolute_error: 0.251 - 0s 225us/step - loss: 0.1070 - mean_absolute_error: 0.2530 - val_loss: 0.0968 - val_mean_absolute_error: 0.2364 Epoch 00278: val_loss did not improve from 0.08486 Epoch 279/5000 102/102 [==============================] - ETA: 0s - loss: 0.1099 - mean_absolute_error: 0.264 - 0s 244us/step - loss: 0.0980 - mean_absolute_error: 0.2465 - val_loss: 0.0976 - val_mean_absolute_error: 0.2376 Epoch 00279: val_loss did not improve from 0.08486 Epoch 280/5000 102/102 [==============================] - ETA: 0s - loss: 0.0900 - mean_absolute_error: 0.229 - 0s 205us/step - loss: 0.0994 - mean_absolute_error: 0.2432 - val_loss: 0.0973 - val_mean_absolute_error: 0.2380 Epoch 00280: val_loss did not improve from 0.08486 Epoch 281/5000 102/102 [==============================] - ETA: 0s - loss: 0.1178 - mean_absolute_error: 0.269 - 0s 215us/step - loss: 0.1157 - mean_absolute_error: 0.2667 - val_loss: 0.0968 - val_mean_absolute_error: 0.2380 Epoch 00281: val_loss did not improve from 0.08486 Epoch 282/5000 102/102 [==============================] - ETA: 0s - loss: 0.1007 - mean_absolute_error: 0.250 - 0s 215us/step - loss: 0.0953 - mean_absolute_error: 0.2369 - val_loss: 0.0955 - val_mean_absolute_error: 0.2375 Epoch 00282: val_loss did not improve from 0.08486 Epoch 283/5000 102/102 [==============================] - ETA: 0s - loss: 0.0994 - mean_absolute_error: 0.246 - 0s 196us/step - loss: 0.1048 - mean_absolute_error: 0.2545 - val_loss: 0.0950 - val_mean_absolute_error: 0.2370 Epoch 00283: val_loss did not improve from 0.08486 Epoch 284/5000 102/102 [==============================] - ETA: 0s - loss: 0.0836 - mean_absolute_error: 0.228 - 0s 225us/step - loss: 0.0922 - mean_absolute_error: 0.2359 - val_loss: 0.0935 - val_mean_absolute_error: 0.2351 Epoch 00284: val_loss did not improve from 0.08486 Epoch 285/5000 102/102 [==============================] - ETA: 0s - loss: 0.1089 - mean_absolute_error: 0.260 - 0s 205us/step - loss: 0.1192 - mean_absolute_error: 0.2690 - val_loss: 0.0917 - val_mean_absolute_error: 0.2332 Epoch 00285: val_loss did not improve from 0.08486 Epoch 286/5000 102/102 [==============================] - ETA: 0s - loss: 0.1175 - mean_absolute_error: 0.248 - 0s 215us/step - loss: 0.0983 - mean_absolute_error: 0.2326 - val_loss: 0.0904 - val_mean_absolute_error: 0.2314 Epoch 00286: val_loss did not improve from 0.08486 Epoch 287/5000 102/102 [==============================] - ETA: 0s - loss: 0.1595 - mean_absolute_error: 0.318 - 0s 215us/step - loss: 0.1171 - mean_absolute_error: 0.2644 - val_loss: 0.0907 - val_mean_absolute_error: 0.2305 Epoch 00287: val_loss did not improve from 0.08486 Epoch 288/5000 102/102 [==============================] - ETA: 0s - loss: 0.1123 - mean_absolute_error: 0.267 - 0s 196us/step - loss: 0.0894 - mean_absolute_error: 0.2348 - val_loss: 0.0925 - val_mean_absolute_error: 0.2308 Epoch 00288: val_loss did not improve from 0.08486 Epoch 289/5000 102/102 [==============================] - ETA: 0s - loss: 0.1171 - mean_absolute_error: 0.257 - 0s 225us/step - loss: 0.0950 - mean_absolute_error: 0.2344 - val_loss: 0.0928 - val_mean_absolute_error: 0.2302 Epoch 00289: val_loss did not improve from 0.08486 Epoch 290/5000 102/102 [==============================] - ETA: 0s - loss: 0.1181 - mean_absolute_error: 0.258 - 0s 215us/step - loss: 0.1168 - mean_absolute_error: 0.2706 - val_loss: 0.0931 - val_mean_absolute_error: 0.2302 Epoch 00290: val_loss did not improve from 0.08486 Epoch 291/5000 102/102 [==============================] - ETA: 0s - loss: 0.0949 - mean_absolute_error: 0.229 - 0s 205us/step - loss: 0.0944 - mean_absolute_error: 0.2346 - val_loss: 0.0925 - val_mean_absolute_error: 0.2299 Epoch 00291: val_loss did not improve from 0.08486 Epoch 292/5000 102/102 [==============================] - ETA: 0s - loss: 0.1049 - mean_absolute_error: 0.258 - 0s 225us/step - loss: 0.1074 - mean_absolute_error: 0.2595 - val_loss: 0.0912 - val_mean_absolute_error: 0.2292 Epoch 00292: val_loss did not improve from 0.08486 Epoch 293/5000 102/102 [==============================] - ETA: 0s - loss: 0.0790 - mean_absolute_error: 0.223 - 0s 205us/step - loss: 0.0918 - mean_absolute_error: 0.2379 - val_loss: 0.0899 - val_mean_absolute_error: 0.2282 Epoch 00293: val_loss did not improve from 0.08486 Epoch 294/5000 102/102 [==============================] - ETA: 0s - loss: 0.0643 - mean_absolute_error: 0.212 - 0s 196us/step - loss: 0.0843 - mean_absolute_error: 0.2291 - val_loss: 0.0910 - val_mean_absolute_error: 0.2299 Epoch 00294: val_loss did not improve from 0.08486 Epoch 295/5000 102/102 [==============================] - ETA: 0s - loss: 0.0887 - mean_absolute_error: 0.230 - 0s 196us/step - loss: 0.1057 - mean_absolute_error: 0.2533 - val_loss: 0.0925 - val_mean_absolute_error: 0.2319 Epoch 00295: val_loss did not improve from 0.08486 Epoch 296/5000 102/102 [==============================] - ETA: 0s - loss: 0.0911 - mean_absolute_error: 0.230 - 0s 215us/step - loss: 0.0957 - mean_absolute_error: 0.2380 - val_loss: 0.0941 - val_mean_absolute_error: 0.2340 Epoch 00296: val_loss did not improve from 0.08486 Epoch 297/5000 102/102 [==============================] - ETA: 0s - loss: 0.0907 - mean_absolute_error: 0.216 - 0s 205us/step - loss: 0.0904 - mean_absolute_error: 0.2266 - val_loss: 0.0956 - val_mean_absolute_error: 0.2358 Epoch 00297: val_loss did not improve from 0.08486 Epoch 298/5000 102/102 [==============================] - ETA: 0s - loss: 0.1061 - mean_absolute_error: 0.266 - 0s 205us/step - loss: 0.1011 - mean_absolute_error: 0.2491 - val_loss: 0.0958 - val_mean_absolute_error: 0.2370 Epoch 00298: val_loss did not improve from 0.08486 Epoch 299/5000 102/102 [==============================] - ETA: 0s - loss: 0.0780 - mean_absolute_error: 0.220 - 0s 205us/step - loss: 0.0940 - mean_absolute_error: 0.2313 - val_loss: 0.0964 - val_mean_absolute_error: 0.2380 Epoch 00299: val_loss did not improve from 0.08486 Epoch 300/5000 102/102 [==============================] - ETA: 0s - loss: 0.1132 - mean_absolute_error: 0.263 - 0s 235us/step - loss: 0.1136 - mean_absolute_error: 0.2631 - val_loss: 0.0956 - val_mean_absolute_error: 0.2377 Epoch 00300: val_loss did not improve from 0.08486 Epoch 301/5000 102/102 [==============================] - ETA: 0s - loss: 0.0822 - mean_absolute_error: 0.220 - 0s 196us/step - loss: 0.0899 - mean_absolute_error: 0.2325 - val_loss: 0.0930 - val_mean_absolute_error: 0.2353 Epoch 00301: val_loss did not improve from 0.08486 Epoch 302/5000 102/102 [==============================] - ETA: 0s - loss: 0.1071 - mean_absolute_error: 0.246 - 0s 235us/step - loss: 0.0845 - mean_absolute_error: 0.2187 - val_loss: 0.0906 - val_mean_absolute_error: 0.2325 Epoch 00302: val_loss did not improve from 0.08486 Epoch 303/5000 102/102 [==============================] - ETA: 0s - loss: 0.1175 - mean_absolute_error: 0.266 - 0s 205us/step - loss: 0.0951 - mean_absolute_error: 0.2344 - val_loss: 0.0892 - val_mean_absolute_error: 0.2306 Epoch 00303: val_loss did not improve from 0.08486 Epoch 304/5000 102/102 [==============================] - ETA: 0s - loss: 0.0936 - mean_absolute_error: 0.220 - 0s 205us/step - loss: 0.0874 - mean_absolute_error: 0.2243 - val_loss: 0.0895 - val_mean_absolute_error: 0.2298 Epoch 00304: val_loss did not improve from 0.08486 Epoch 305/5000 102/102 [==============================] - ETA: 0s - loss: 0.0886 - mean_absolute_error: 0.230 - 0s 205us/step - loss: 0.0819 - mean_absolute_error: 0.2142 - val_loss: 0.0914 - val_mean_absolute_error: 0.2301 Epoch 00305: val_loss did not improve from 0.08486 Epoch 306/5000 102/102 [==============================] - ETA: 0s - loss: 0.1039 - mean_absolute_error: 0.248 - 0s 176us/step - loss: 0.0960 - mean_absolute_error: 0.2372 - val_loss: 0.0919 - val_mean_absolute_error: 0.2294 Epoch 00306: val_loss did not improve from 0.08486 Epoch 307/5000 102/102 [==============================] - ETA: 0s - loss: 0.0746 - mean_absolute_error: 0.207 - 0s 205us/step - loss: 0.0892 - mean_absolute_error: 0.2196 - val_loss: 0.0902 - val_mean_absolute_error: 0.2273 Epoch 00307: val_loss did not improve from 0.08486 Epoch 308/5000 102/102 [==============================] - ETA: 0s - loss: 0.1091 - mean_absolute_error: 0.252 - 0s 196us/step - loss: 0.0970 - mean_absolute_error: 0.2445 - val_loss: 0.0882 - val_mean_absolute_error: 0.2253 Epoch 00308: val_loss did not improve from 0.08486 Epoch 309/5000 102/102 [==============================] - ETA: 0s - loss: 0.1127 - mean_absolute_error: 0.254 - 0s 215us/step - loss: 0.0957 - mean_absolute_error: 0.2338 - val_loss: 0.0884 - val_mean_absolute_error: 0.2259 Epoch 00309: val_loss did not improve from 0.08486 Epoch 310/5000 102/102 [==============================] - ETA: 0s - loss: 0.1178 - mean_absolute_error: 0.277 - 0s 215us/step - loss: 0.0919 - mean_absolute_error: 0.2347 - val_loss: 0.0909 - val_mean_absolute_error: 0.2287 Epoch 00310: val_loss did not improve from 0.08486 Epoch 311/5000 102/102 [==============================] - ETA: 0s - loss: 0.0733 - mean_absolute_error: 0.214 - 0s 205us/step - loss: 0.0930 - mean_absolute_error: 0.2391 - val_loss: 0.0911 - val_mean_absolute_error: 0.2294 Epoch 00311: val_loss did not improve from 0.08486 Epoch 312/5000 102/102 [==============================] - ETA: 0s - loss: 0.1134 - mean_absolute_error: 0.265 - 0s 196us/step - loss: 0.1077 - mean_absolute_error: 0.2512 - val_loss: 0.0904 - val_mean_absolute_error: 0.2294 Epoch 00312: val_loss did not improve from 0.08486 Epoch 313/5000 102/102 [==============================] - ETA: 0s - loss: 0.0974 - mean_absolute_error: 0.241 - 0s 186us/step - loss: 0.0774 - mean_absolute_error: 0.2103 - val_loss: 0.0901 - val_mean_absolute_error: 0.2293 Epoch 00313: val_loss did not improve from 0.08486 Epoch 314/5000 102/102 [==============================] - ETA: 0s - loss: 0.0747 - mean_absolute_error: 0.214 - 0s 205us/step - loss: 0.0825 - mean_absolute_error: 0.2216 - val_loss: 0.0902 - val_mean_absolute_error: 0.2295 Epoch 00314: val_loss did not improve from 0.08486 Epoch 315/5000 102/102 [==============================] - ETA: 0s - loss: 0.0809 - mean_absolute_error: 0.215 - 0s 205us/step - loss: 0.0819 - mean_absolute_error: 0.2213 - val_loss: 0.0897 - val_mean_absolute_error: 0.2289 Epoch 00315: val_loss did not improve from 0.08486 Epoch 316/5000 102/102 [==============================] - ETA: 0s - loss: 0.1015 - mean_absolute_error: 0.243 - 0s 215us/step - loss: 0.0956 - mean_absolute_error: 0.2466 - val_loss: 0.0900 - val_mean_absolute_error: 0.2290 Epoch 00316: val_loss did not improve from 0.08486 Epoch 317/5000 102/102 [==============================] - ETA: 0s - loss: 0.0823 - mean_absolute_error: 0.227 - 0s 186us/step - loss: 0.0852 - mean_absolute_error: 0.2269 - val_loss: 0.0906 - val_mean_absolute_error: 0.2285 Epoch 00317: val_loss did not improve from 0.08486 Epoch 318/5000 102/102 [==============================] - ETA: 0s - loss: 0.0880 - mean_absolute_error: 0.226 - 0s 215us/step - loss: 0.0890 - mean_absolute_error: 0.2331 - val_loss: 0.0914 - val_mean_absolute_error: 0.2285 Epoch 00318: val_loss did not improve from 0.08486 Epoch 319/5000 102/102 [==============================] - ETA: 0s - loss: 0.1118 - mean_absolute_error: 0.262 - 0s 215us/step - loss: 0.1034 - mean_absolute_error: 0.2542 - val_loss: 0.0931 - val_mean_absolute_error: 0.2303 Epoch 00319: val_loss did not improve from 0.08486 Epoch 320/5000 102/102 [==============================] - ETA: 0s - loss: 0.0752 - mean_absolute_error: 0.198 - 0s 205us/step - loss: 0.1024 - mean_absolute_error: 0.2412 - val_loss: 0.0929 - val_mean_absolute_error: 0.2303 Epoch 00320: val_loss did not improve from 0.08486 Epoch 321/5000 102/102 [==============================] - ETA: 0s - loss: 0.0717 - mean_absolute_error: 0.197 - 0s 196us/step - loss: 0.0865 - mean_absolute_error: 0.2243 - val_loss: 0.0915 - val_mean_absolute_error: 0.2292 Epoch 00321: val_loss did not improve from 0.08486 Epoch 322/5000 102/102 [==============================] - ETA: 0s - loss: 0.0875 - mean_absolute_error: 0.211 - 0s 205us/step - loss: 0.0781 - mean_absolute_error: 0.2098 - val_loss: 0.0900 - val_mean_absolute_error: 0.2279 Epoch 00322: val_loss did not improve from 0.08486 Epoch 323/5000 102/102 [==============================] - ETA: 0s - loss: 0.0794 - mean_absolute_error: 0.220 - 0s 215us/step - loss: 0.0883 - mean_absolute_error: 0.2336 - val_loss: 0.0894 - val_mean_absolute_error: 0.2269 Epoch 00323: val_loss did not improve from 0.08486 Epoch 324/5000 102/102 [==============================] - ETA: 0s - loss: 0.1084 - mean_absolute_error: 0.248 - 0s 205us/step - loss: 0.1024 - mean_absolute_error: 0.2455 - val_loss: 0.0889 - val_mean_absolute_error: 0.2261 Epoch 00324: val_loss did not improve from 0.08486 Epoch 325/5000 102/102 [==============================] - ETA: 0s - loss: 0.1003 - mean_absolute_error: 0.233 - 0s 205us/step - loss: 0.0985 - mean_absolute_error: 0.2406 - val_loss: 0.0887 - val_mean_absolute_error: 0.2258 Epoch 00325: val_loss did not improve from 0.08486 Epoch 326/5000 102/102 [==============================] - ETA: 0s - loss: 0.0909 - mean_absolute_error: 0.233 - 0s 225us/step - loss: 0.0778 - mean_absolute_error: 0.2144 - val_loss: 0.0901 - val_mean_absolute_error: 0.2267 Epoch 00326: val_loss did not improve from 0.08486 Epoch 327/5000 102/102 [==============================] - ETA: 0s - loss: 0.0783 - mean_absolute_error: 0.216 - 0s 196us/step - loss: 0.0795 - mean_absolute_error: 0.2132 - val_loss: 0.0908 - val_mean_absolute_error: 0.2274 Epoch 00327: val_loss did not improve from 0.08486 Epoch 328/5000 102/102 [==============================] - ETA: 0s - loss: 0.0831 - mean_absolute_error: 0.217 - 0s 205us/step - loss: 0.0887 - mean_absolute_error: 0.2257 - val_loss: 0.0914 - val_mean_absolute_error: 0.2281 Epoch 00328: val_loss did not improve from 0.08486 Epoch 329/5000 102/102 [==============================] - ETA: 0s - loss: 0.0959 - mean_absolute_error: 0.235 - 0s 186us/step - loss: 0.0969 - mean_absolute_error: 0.2449 - val_loss: 0.0921 - val_mean_absolute_error: 0.2291 Epoch 00329: val_loss did not improve from 0.08486 Epoch 330/5000 102/102 [==============================] - ETA: 0s - loss: 0.0737 - mean_absolute_error: 0.209 - 0s 205us/step - loss: 0.0928 - mean_absolute_error: 0.2350 - val_loss: 0.0914 - val_mean_absolute_error: 0.2292 Epoch 00330: val_loss did not improve from 0.08486 Epoch 331/5000 102/102 [==============================] - ETA: 0s - loss: 0.0901 - mean_absolute_error: 0.236 - 0s 235us/step - loss: 0.0970 - mean_absolute_error: 0.2378 - val_loss: 0.0895 - val_mean_absolute_error: 0.2285 Epoch 00331: val_loss did not improve from 0.08486 Epoch 332/5000 102/102 [==============================] - ETA: 0s - loss: 0.0835 - mean_absolute_error: 0.216 - 0s 215us/step - loss: 0.1033 - mean_absolute_error: 0.2427 - val_loss: 0.0868 - val_mean_absolute_error: 0.2268 Epoch 00332: val_loss did not improve from 0.08486 Epoch 333/5000 102/102 [==============================] - ETA: 0s - loss: 0.0775 - mean_absolute_error: 0.219 - 0s 215us/step - loss: 0.0817 - mean_absolute_error: 0.2201 - val_loss: 0.0850 - val_mean_absolute_error: 0.2250 Epoch 00333: val_loss did not improve from 0.08486 Epoch 334/5000 102/102 [==============================] - ETA: 0s - loss: 0.0679 - mean_absolute_error: 0.204 - 0s 205us/step - loss: 0.0745 - mean_absolute_error: 0.2115 - val_loss: 0.0863 - val_mean_absolute_error: 0.2260 Epoch 00334: val_loss did not improve from 0.08486 Epoch 335/5000 102/102 [==============================] - ETA: 0s - loss: 0.0778 - mean_absolute_error: 0.223 - 0s 215us/step - loss: 0.0953 - mean_absolute_error: 0.2383 - val_loss: 0.0883 - val_mean_absolute_error: 0.2277 Epoch 00335: val_loss did not improve from 0.08486 Epoch 336/5000 102/102 [==============================] - ETA: 0s - loss: 0.0928 - mean_absolute_error: 0.233 - 0s 195us/step - loss: 0.0794 - mean_absolute_error: 0.2180 - val_loss: 0.0906 - val_mean_absolute_error: 0.2297 Epoch 00336: val_loss did not improve from 0.08486 Epoch 337/5000 102/102 [==============================] - ETA: 0s - loss: 0.0785 - mean_absolute_error: 0.212 - 0s 215us/step - loss: 0.0876 - mean_absolute_error: 0.2199 - val_loss: 0.0914 - val_mean_absolute_error: 0.2305 Epoch 00337: val_loss did not improve from 0.08486 Epoch 338/5000 102/102 [==============================] - ETA: 0s - loss: 0.1006 - mean_absolute_error: 0.236 - 0s 215us/step - loss: 0.0896 - mean_absolute_error: 0.2275 - val_loss: 0.0920 - val_mean_absolute_error: 0.2311 Epoch 00338: val_loss did not improve from 0.08486 Epoch 339/5000 102/102 [==============================] - ETA: 0s - loss: 0.0979 - mean_absolute_error: 0.236 - 0s 205us/step - loss: 0.0780 - mean_absolute_error: 0.2145 - val_loss: 0.0933 - val_mean_absolute_error: 0.2328 Epoch 00339: val_loss did not improve from 0.08486 Epoch 340/5000 102/102 [==============================] - ETA: 0s - loss: 0.0878 - mean_absolute_error: 0.229 - 0s 195us/step - loss: 0.0969 - mean_absolute_error: 0.2370 - val_loss: 0.0929 - val_mean_absolute_error: 0.2328 Epoch 00340: val_loss did not improve from 0.08486 Epoch 341/5000 102/102 [==============================] - ETA: 0s - loss: 0.0825 - mean_absolute_error: 0.224 - 0s 196us/step - loss: 0.0796 - mean_absolute_error: 0.2146 - val_loss: 0.0918 - val_mean_absolute_error: 0.2323 Epoch 00341: val_loss did not improve from 0.08486 Epoch 342/5000 102/102 [==============================] - ETA: 0s - loss: 0.1210 - mean_absolute_error: 0.277 - 0s 215us/step - loss: 0.1080 - mean_absolute_error: 0.2554 - val_loss: 0.0899 - val_mean_absolute_error: 0.2302 Epoch 00342: val_loss did not improve from 0.08486 Epoch 343/5000 102/102 [==============================] - ETA: 0s - loss: 0.0821 - mean_absolute_error: 0.215 - 0s 225us/step - loss: 0.1061 - mean_absolute_error: 0.2498 - val_loss: 0.0885 - val_mean_absolute_error: 0.2279 Epoch 00343: val_loss did not improve from 0.08486 Epoch 344/5000 102/102 [==============================] - ETA: 0s - loss: 0.1262 - mean_absolute_error: 0.268 - 0s 196us/step - loss: 0.0913 - mean_absolute_error: 0.2302 - val_loss: 0.0880 - val_mean_absolute_error: 0.2267 Epoch 00344: val_loss did not improve from 0.08486 Epoch 345/5000 102/102 [==============================] - ETA: 0s - loss: 0.1046 - mean_absolute_error: 0.249 - 0s 196us/step - loss: 0.0950 - mean_absolute_error: 0.2426 - val_loss: 0.0897 - val_mean_absolute_error: 0.2285 Epoch 00345: val_loss did not improve from 0.08486 Epoch 346/5000 102/102 [==============================] - ETA: 0s - loss: 0.0784 - mean_absolute_error: 0.221 - 0s 196us/step - loss: 0.0830 - mean_absolute_error: 0.2228 - val_loss: 0.0927 - val_mean_absolute_error: 0.2308 Epoch 00346: val_loss did not improve from 0.08486 Epoch 347/5000 102/102 [==============================] - ETA: 0s - loss: 0.0998 - mean_absolute_error: 0.242 - 0s 176us/step - loss: 0.0940 - mean_absolute_error: 0.2340 - val_loss: 0.0944 - val_mean_absolute_error: 0.2315 Epoch 00347: val_loss did not improve from 0.08486 Epoch 348/5000 102/102 [==============================] - ETA: 0s - loss: 0.0855 - mean_absolute_error: 0.212 - 0s 186us/step - loss: 0.0914 - mean_absolute_error: 0.2254 - val_loss: 0.0940 - val_mean_absolute_error: 0.2303 Epoch 00348: val_loss did not improve from 0.08486 Epoch 349/5000 102/102 [==============================] - ETA: 0s - loss: 0.0883 - mean_absolute_error: 0.224 - 0s 205us/step - loss: 0.0855 - mean_absolute_error: 0.2237 - val_loss: 0.0937 - val_mean_absolute_error: 0.2297 Epoch 00349: val_loss did not improve from 0.08486 Epoch 350/5000 102/102 [==============================] - ETA: 0s - loss: 0.0725 - mean_absolute_error: 0.204 - 0s 225us/step - loss: 0.0841 - mean_absolute_error: 0.2133 - val_loss: 0.0947 - val_mean_absolute_error: 0.2307 Epoch 00350: val_loss did not improve from 0.08486 Epoch 351/5000 102/102 [==============================] - ETA: 0s - loss: 0.0821 - mean_absolute_error: 0.213 - 0s 205us/step - loss: 0.0812 - mean_absolute_error: 0.2210 - val_loss: 0.0941 - val_mean_absolute_error: 0.2316 Epoch 00351: val_loss did not improve from 0.08486 Epoch 352/5000 102/102 [==============================] - ETA: 0s - loss: 0.1161 - mean_absolute_error: 0.280 - 0s 186us/step - loss: 0.0930 - mean_absolute_error: 0.2404 - val_loss: 0.0915 - val_mean_absolute_error: 0.2308 Epoch 00352: val_loss did not improve from 0.08486 Epoch 353/5000 102/102 [==============================] - ETA: 0s - loss: 0.0744 - mean_absolute_error: 0.189 - 0s 196us/step - loss: 0.0947 - mean_absolute_error: 0.2338 - val_loss: 0.0893 - val_mean_absolute_error: 0.2291 Epoch 00353: val_loss did not improve from 0.08486 Epoch 354/5000 102/102 [==============================] - ETA: 0s - loss: 0.0863 - mean_absolute_error: 0.239 - 0s 205us/step - loss: 0.0831 - mean_absolute_error: 0.2294 - val_loss: 0.0883 - val_mean_absolute_error: 0.2281 Epoch 00354: val_loss did not improve from 0.08486 Epoch 355/5000 102/102 [==============================] - ETA: 0s - loss: 0.1356 - mean_absolute_error: 0.294 - 0s 205us/step - loss: 0.0952 - mean_absolute_error: 0.2425 - val_loss: 0.0898 - val_mean_absolute_error: 0.2288 Epoch 00355: val_loss did not improve from 0.08486 Epoch 356/5000 102/102 [==============================] - ETA: 0s - loss: 0.0963 - mean_absolute_error: 0.237 - 0s 225us/step - loss: 0.0974 - mean_absolute_error: 0.2401 - val_loss: 0.0930 - val_mean_absolute_error: 0.2316 Epoch 00356: val_loss did not improve from 0.08486 Epoch 357/5000 102/102 [==============================] - ETA: 0s - loss: 0.0845 - mean_absolute_error: 0.230 - 0s 205us/step - loss: 0.0811 - mean_absolute_error: 0.2228 - val_loss: 0.0942 - val_mean_absolute_error: 0.2330 Epoch 00357: val_loss did not improve from 0.08486 Epoch 358/5000 102/102 [==============================] - ETA: 0s - loss: 0.1079 - mean_absolute_error: 0.264 - 0s 215us/step - loss: 0.0936 - mean_absolute_error: 0.2396 - val_loss: 0.0938 - val_mean_absolute_error: 0.2327 Epoch 00358: val_loss did not improve from 0.08486 13/13 [==============================] - 0s 153us/step Loss: 0.06686072796583176 MAE: 0.19180764257907867
In [13]:
from PIL import Image
import matplotlib.pyplot as plt
%matplotlib inline
for i in os.listdir('find_phone_test_images'):
img1=Image.open('find_phone_test_images/'+i)
img=img1.resize((224,224))
'''img=np.array(img1).astype('float')
img -= img.min() # shifted to 0..max
img *= 1 / img.max()
'''
test_img=np.expand_dims(img,axis=0)
#print(test_img.shape)
test_input=model2.predict(preprocess_input(test_img))
loc=dnn_model.predict(test_input)
print(loc[0])
visualize(img1,loc[0])
plt.show()
[0.25180376 0.24587122] 50 93 110 153
[0.21660244 0.18191382] 29 76 89 136
[0.30478472 0.38343698] 95 119 155 179
[0.25425056 0.12619677] 11 94 71 154
[0.19728512 0.06156806] -10 66 50 126
[0.06280089 0.419859 ] 106 0 166 60
[-0.06679661 0.5259008 ] 141 -62 201 -2
[0.413471 0.22051859] 41 172 101 232
[0.22357242 0.32039952] 74 79 134 139
[0.23103258 0.32572904] 76 83 136 143
[0.17806214 0.21014671] 38 57 98 117
[0.18638578 0.25292912] 52 61 112 121
[0.3775662 0.23530316] 46 155 106 215
[0.38745657 0.38839215] 96 159 156 219
[0.4196905 0.22812566] 44 175 104 235
[0.46398506 0.38495955] 95 197 155 257
[0.1338747 0.1944781] 33 35 93 95
[0.19923347 0.15037261] 19 67 79 127
[0.64766204 0.2912062 ] 64 287 124 347
[0.344916 0.36135477] 87 139 147 199
In [14]:
def test_train_dev_split2(input_data, output_data, train=0.8, dev=0.1,
test=0.1):
#make seed for exact results everything
#input_data=preprocess_input(input_data)
input_data, output_data = shuffle(input_data, output_data, random_state=0)
for num in range(0,len(input_data)):
input_data[num]=np.array(input_data[num].resize((224,224))).astype('float')
input_data[num] -= input_data[num].min() # shifted to 0..max
input_data[num] *= 1 / input_data[num].max()
split1 = int(train * len(input_data))
split2 = int((train + dev) * len(input_data))
train_input = input_data[:split1]
dev_input = input_data[split1:split2]
test_input = input_data[split2:]
train_output = output_data[:split1]
dev_output = output_data[split1:split2]
test_output = output_data[split2:]
print(train_input[0])
return np.array(train_input),np.array(train_output),np.array(dev_input), np.array(dev_output),np.array(test_input), np.array(test_output)
In [ ]:
In [15]:
def r2_keras(y_true, y_pred):
SS_res = K.sum(K.square(y_true - y_pred))
SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
return ( 1 - SS_res/(SS_tot + K.epsilon()) )
In [ ]:
In [16]:
# Experiment 2
"""
'Direct model: raw image classification'
"""
def phone_finder_model_cnn(input_data, output_data):
train_in, train_out, dev_in, dev_out, test_in, test_out = test_train_dev_split2(
input_data, output_data)
#https://arxiv.org/pdf/1509.05371v2.pdf
#print(np.array(train_in).shape)
model=Sequential()
model.add(Conv2D(5,110,activation='relu',input_shape=(224,224,3)))
model.add(MaxPool2D())
model.add(Dropout(0.4))
'''model.add(Conv2D(9,55,activation='relu'))
model.add(MaxPool2D())
model.add(Dropout(0.4))'''
model.add(Conv2D(3,27,activation='relu'))
model.add(MaxPool2D())
model.add(Dropout(0.4))
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(8,activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(2,activation='linear'))
print(model.input_shape,model.output_shape)
print(model.summary())
'''
model.compile(
optimizer=adam(0.001),
loss=[focal_loss(alpha=.25, gamma=2)],
metrics=['accuracy'])
'''
model.compile(
optimizer=adam(0.0001),
loss='mse',
metrics=['mae'])
early = EarlyStopping(patience=5)
check = ModelCheckpoint(
'find_phone_cnn_model.h5',
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
model_history=model.fit(
train_in,
train_out,
batch_size=32,
callbacks=[early, check],
validation_data=(dev_in, dev_out),
epochs=5000)
loss,mse = model.evaluate(test_in, test_out)
print("Loss: {0} MSE: {1}".format(loss, mse))
plt.plot(model_history.history['loss'])
plt.plot(model_history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
return model
model=phone_finder_model_cnn(input_data,labels)
[[[0.41568627 0.39607843 0.38039216] [0.46666667 0.44705882 0.43137255] [0.4627451 0.44313725 0.42745098] ... [0.00392157 0.00392157 0.00392157] [0.00392157 0.00392157 0.00392157] [0.00784314 0.00784314 0.00784314]] [[0.47058824 0.45098039 0.43529412] [0.45490196 0.43529412 0.41960784] [0.4627451 0.44313725 0.42745098] ... [0.00784314 0.00784314 0.00784314] [0. 0. 0. ] [0. 0. 0. ]] [[0.45490196 0.43529412 0.41960784] [0.45098039 0.43137255 0.41568627] [0.45490196 0.43529412 0.41960784] ... [0.00784314 0.00784314 0.00784314] [0.00392157 0.00392157 0.00392157] [0. 0. 0. ]] ... [[0.3254902 0.29411765 0.20392157] [0.26666667 0.23529412 0.18431373] [0.18823529 0.16078431 0.12156863] ... [0.4 0.38823529 0.36862745] [0.4 0.38823529 0.36862745] [0.38431373 0.37254902 0.34509804]] [[0.34117647 0.30980392 0.21960784] [0.23137255 0.2 0.14901961] [0.21960784 0.19215686 0.15294118] ... [0.4 0.38823529 0.36862745] [0.4 0.38823529 0.36862745] [0.39215686 0.38039216 0.35294118]] [[0.30980392 0.27843137 0.18823529] [0.23921569 0.20784314 0.15686275] [0.20392157 0.17647059 0.1372549 ] ... [0.4 0.38823529 0.36862745] [0.39607843 0.38431373 0.36470588] [0.4 0.38823529 0.36078431]]] (None, 224, 224, 3) (None, 2) _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 115, 115, 5) 181505 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 57, 57, 5) 0 _________________________________________________________________ dropout_3 (Dropout) (None, 57, 57, 5) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 31, 31, 3) 10938 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 15, 15, 3) 0 _________________________________________________________________ dropout_4 (Dropout) (None, 15, 15, 3) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 675) 0 _________________________________________________________________ batch_normalization_1 (Batch (None, 675) 2700 _________________________________________________________________ dense_4 (Dense) (None, 8) 5408 _________________________________________________________________ dropout_5 (Dropout) (None, 8) 0 _________________________________________________________________ dense_5 (Dense) (None, 2) 18 ================================================================= Total params: 200,569 Trainable params: 199,219 Non-trainable params: 1,350 _________________________________________________________________ None Train on 102 samples, validate on 13 samples Epoch 1/5000 102/102 [==============================] - ETA: 14s - loss: 2.1155 - mean_absolute_error: 1.23 - ETA: 3s - loss: 2.5242 - mean_absolute_error: 1.3257 - ETA: 0s - loss: 2.5356 - mean_absolute_error: 1.298 - 9s 88ms/step - loss: 2.5366 - mean_absolute_error: 1.2998 - val_loss: 0.4133 - val_mean_absolute_error: 0.5798 Epoch 00001: val_loss improved from inf to 0.41325, saving model to find_phone_cnn_model.h5 Epoch 2/5000 102/102 [==============================] - ETA: 0s - loss: 1.6639 - mean_absolute_error: 1.071 - ETA: 0s - loss: 1.7640 - mean_absolute_error: 1.090 - ETA: 0s - loss: 1.9604 - mean_absolute_error: 1.147 - 0s 3ms/step - loss: 2.0141 - mean_absolute_error: 1.1531 - val_loss: 0.3665 - val_mean_absolute_error: 0.5235 Epoch 00002: val_loss improved from 0.41325 to 0.36654, saving model to find_phone_cnn_model.h5 Epoch 3/5000 102/102 [==============================] - ETA: 0s - loss: 2.7535 - mean_absolute_error: 1.260 - ETA: 0s - loss: 2.5379 - mean_absolute_error: 1.262 - ETA: 0s - loss: 2.5007 - mean_absolute_error: 1.238 - 0s 3ms/step - loss: 2.4500 - mean_absolute_error: 1.2307 - val_loss: 0.3444 - val_mean_absolute_error: 0.5071 Epoch 00003: val_loss improved from 0.36654 to 0.34440, saving model to find_phone_cnn_model.h5 Epoch 4/5000 102/102 [==============================] - ETA: 0s - loss: 2.2843 - mean_absolute_error: 1.259 - ETA: 0s - loss: 2.5642 - mean_absolute_error: 1.313 - ETA: 0s - loss: 2.1790 - mean_absolute_error: 1.190 - 0s 3ms/step - loss: 2.2113 - mean_absolute_error: 1.1986 - val_loss: 0.3670 - val_mean_absolute_error: 0.5243 Epoch 00004: val_loss did not improve from 0.34440 Epoch 5/5000 102/102 [==============================] - ETA: 0s - loss: 1.5093 - mean_absolute_error: 1.065 - ETA: 0s - loss: 1.8866 - mean_absolute_error: 1.103 - ETA: 0s - loss: 1.9943 - mean_absolute_error: 1.107 - 0s 3ms/step - loss: 1.9682 - mean_absolute_error: 1.1026 - val_loss: 0.4267 - val_mean_absolute_error: 0.5688 Epoch 00005: val_loss did not improve from 0.34440 Epoch 6/5000 102/102 [==============================] - ETA: 0s - loss: 1.8942 - mean_absolute_error: 1.130 - ETA: 0s - loss: 1.7241 - mean_absolute_error: 1.046 - ETA: 0s - loss: 1.9092 - mean_absolute_error: 1.125 - 0s 3ms/step - loss: 1.8540 - mean_absolute_error: 1.1085 - val_loss: 0.4286 - val_mean_absolute_error: 0.5763 Epoch 00006: val_loss did not improve from 0.34440 Epoch 7/5000 102/102 [==============================] - ETA: 0s - loss: 2.0933 - mean_absolute_error: 1.147 - ETA: 0s - loss: 1.9592 - mean_absolute_error: 1.113 - ETA: 0s - loss: 1.9852 - mean_absolute_error: 1.140 - 0s 3ms/step - loss: 1.9889 - mean_absolute_error: 1.1442 - val_loss: 0.4589 - val_mean_absolute_error: 0.5984 Epoch 00007: val_loss did not improve from 0.34440 Epoch 8/5000 102/102 [==============================] - ETA: 0s - loss: 1.5311 - mean_absolute_error: 0.954 - ETA: 0s - loss: 1.6952 - mean_absolute_error: 1.061 - ETA: 0s - loss: 2.0373 - mean_absolute_error: 1.145 - 0s 3ms/step - loss: 2.0467 - mean_absolute_error: 1.1559 - val_loss: 0.5185 - val_mean_absolute_error: 0.6447 Epoch 00008: val_loss did not improve from 0.34440 13/13 [==============================] - 0s 2ms/step Loss: 0.44583988189697266 MSE: 0.6416805982589722
In [19]:
from PIL import Image
import matplotlib.pyplot as plt
%matplotlib inline
for i in os.listdir('find_phone_test_images'):
img1=Image.open('find_phone_test_images/'+i).resize((224,224))
img=np.array(img1).astype('float')
img -= img.min() # shifted to 0..max
img *= 1 / img.max()
test_img=np.expand_dims(img,axis=0)
#print(test_img.shape)
#test_input=model2.predict(preprocess_input(test_img))
loc=model.predict(test_img)
print(loc[0])
visualize(img1,loc[0])
plt.show()
[-0.22252253 -0.4727551 ] -135 -79 -75 -19
[-0.25442266 -0.56584257] -156 -86 -96 -26
[ 0.08178521 -0.07145099] -46 -12 14 48
[-0.06284944 -0.234316 ] -82 -44 -22 16
[-0.11512952 -0.36125222] -110 -55 -50 5
[-0.04948209 -0.23442566] -82 -41 -22 19
[-0.23251142 -0.5049254 ] -143 -82 -83 -22
[ 0.01024407 -0.13855934] -61 -28 -1 32
[ 0.3602733 -0.19078279] -72 50 -12 110
[ 0.1403128 -0.11066281] -54 1 6 61
[ 0.21775466 -0.13930482] -61 18 -1 78
[-0.12087735 -0.36032325] -110 -57 -50 3
[ 0.05900535 -0.0642394 ] -44 -17 16 43
[ 0.22330843 -0.12824185] -58 20 2 80
[-0.10528253 -0.30882022] -99 -53 -39 7
[-0.30555937 -0.626063 ] -170 -98 -110 -38
[ 0.32893223 -0.17482455] -69 43 -9 103
[-0.09071043 -0.28727838] -94 -50 -34 10
[-0.21020977 -0.47720614] -136 -77 -76 -17
[-0.3285564 -0.66774964] -179 -103 -119 -43
In [ ]:
In [ ]:
'''#Since we have very less images, Let's try Image augmentation to get some more data but Then how to get the coordinates?
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
for num in range(0,len(input_data)):
input_data[num]=np.array(input_data[num])
print(np.array(input_data).shape)
i=0
for batch in datagen.flow(np.array(input_data), batch_size=1,
save_to_dir='augmented_imgs', save_prefix='aug', save_format='jpeg'):
i += 1
if i > 20:
break # otherwise the generator would loop indefinitely
'''
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In [ ]:
In [ ]: